{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Introduction: Feature Selector Usage\n",
    "\n",
    "In this notebook we will walk-through using the `FeatureSelector` class for selecting features to remove from a dataset. This class has five methods for finding features to remove: \n",
    "\n",
    "1. Find columns with a missing fraction greater than a specified threshold\n",
    "2. Find features with only a single unique value\n",
    "3. Find collinear features as identified by a correlation coefficient greater than a specified value\n",
    "4. Find features with 0.0 importance from a gradient boosting machine\n",
    "5. Find features that do not contribute to a specified cumulative feature importance from the gradient boosting machine\n",
    "\n",
    "The FeatureSelector is a work in progress! Any contributions on GitHub are appreciated."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from feature_selector import FeatureSelector\n",
    "\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Example Dataset\n",
    "\n",
    "This dataset was used as part of the [Home Credit Default Risk competition on Kaggle](https://www.kaggle.com/c/home-credit-default-risk/). It is intended for a supervised machine learning classification task where the objective is to predict if a client will default on a loan. The entire dataset can be downloaded [here] and we will work with a small sample of 10,000 rows. \n",
    "\n",
    "The feature selector was designed to be used for machine learning tasks, but can be applied to any dataset.\\ The feature importance based methods do require a supervised machine learning problem."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SK_ID_CURR</th>\n",
       "      <th>TARGET</th>\n",
       "      <th>NAME_CONTRACT_TYPE</th>\n",
       "      <th>CODE_GENDER</th>\n",
       "      <th>FLAG_OWN_CAR</th>\n",
       "      <th>FLAG_OWN_REALTY</th>\n",
       "      <th>CNT_CHILDREN</th>\n",
       "      <th>AMT_INCOME_TOTAL</th>\n",
       "      <th>AMT_CREDIT</th>\n",
       "      <th>AMT_ANNUITY</th>\n",
       "      <th>...</th>\n",
       "      <th>FLAG_DOCUMENT_18</th>\n",
       "      <th>FLAG_DOCUMENT_19</th>\n",
       "      <th>FLAG_DOCUMENT_20</th>\n",
       "      <th>FLAG_DOCUMENT_21</th>\n",
       "      <th>AMT_REQ_CREDIT_BUREAU_HOUR</th>\n",
       "      <th>AMT_REQ_CREDIT_BUREAU_DAY</th>\n",
       "      <th>AMT_REQ_CREDIT_BUREAU_WEEK</th>\n",
       "      <th>AMT_REQ_CREDIT_BUREAU_MON</th>\n",
       "      <th>AMT_REQ_CREDIT_BUREAU_QRT</th>\n",
       "      <th>AMT_REQ_CREDIT_BUREAU_YEAR</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>247408</td>\n",
       "      <td>0</td>\n",
       "      <td>Cash loans</td>\n",
       "      <td>F</td>\n",
       "      <td>Y</td>\n",
       "      <td>N</td>\n",
       "      <td>2</td>\n",
       "      <td>108000.0</td>\n",
       "      <td>172512.0</td>\n",
       "      <td>13477.5</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>153916</td>\n",
       "      <td>0</td>\n",
       "      <td>Revolving loans</td>\n",
       "      <td>F</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>2</td>\n",
       "      <td>135000.0</td>\n",
       "      <td>180000.0</td>\n",
       "      <td>9000.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>229065</td>\n",
       "      <td>0</td>\n",
       "      <td>Cash loans</td>\n",
       "      <td>F</td>\n",
       "      <td>N</td>\n",
       "      <td>Y</td>\n",
       "      <td>0</td>\n",
       "      <td>112500.0</td>\n",
       "      <td>463500.0</td>\n",
       "      <td>20547.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>282013</td>\n",
       "      <td>0</td>\n",
       "      <td>Cash loans</td>\n",
       "      <td>F</td>\n",
       "      <td>N</td>\n",
       "      <td>Y</td>\n",
       "      <td>0</td>\n",
       "      <td>135000.0</td>\n",
       "      <td>549882.0</td>\n",
       "      <td>17739.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>142266</td>\n",
       "      <td>0</td>\n",
       "      <td>Cash loans</td>\n",
       "      <td>F</td>\n",
       "      <td>N</td>\n",
       "      <td>Y</td>\n",
       "      <td>0</td>\n",
       "      <td>90000.0</td>\n",
       "      <td>518562.0</td>\n",
       "      <td>20695.5</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 122 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   SK_ID_CURR  TARGET NAME_CONTRACT_TYPE CODE_GENDER FLAG_OWN_CAR  \\\n",
       "0      247408       0         Cash loans           F            Y   \n",
       "1      153916       0    Revolving loans           F            Y   \n",
       "2      229065       0         Cash loans           F            N   \n",
       "3      282013       0         Cash loans           F            N   \n",
       "4      142266       0         Cash loans           F            N   \n",
       "\n",
       "  FLAG_OWN_REALTY  CNT_CHILDREN  AMT_INCOME_TOTAL  AMT_CREDIT  AMT_ANNUITY  \\\n",
       "0               N             2          108000.0    172512.0      13477.5   \n",
       "1               Y             2          135000.0    180000.0       9000.0   \n",
       "2               Y             0          112500.0    463500.0      20547.0   \n",
       "3               Y             0          135000.0    549882.0      17739.0   \n",
       "4               Y             0           90000.0    518562.0      20695.5   \n",
       "\n",
       "              ...              FLAG_DOCUMENT_18 FLAG_DOCUMENT_19  \\\n",
       "0             ...                             0                0   \n",
       "1             ...                             0                0   \n",
       "2             ...                             0                0   \n",
       "3             ...                             0                0   \n",
       "4             ...                             0                0   \n",
       "\n",
       "  FLAG_DOCUMENT_20 FLAG_DOCUMENT_21 AMT_REQ_CREDIT_BUREAU_HOUR  \\\n",
       "0                0                0                        0.0   \n",
       "1                0                0                        0.0   \n",
       "2                0                0                        0.0   \n",
       "3                0                0                        0.0   \n",
       "4                0                0                        0.0   \n",
       "\n",
       "  AMT_REQ_CREDIT_BUREAU_DAY  AMT_REQ_CREDIT_BUREAU_WEEK  \\\n",
       "0                       0.0                         0.0   \n",
       "1                       0.0                         0.0   \n",
       "2                       0.0                         1.0   \n",
       "3                       0.0                         0.0   \n",
       "4                       0.0                         0.0   \n",
       "\n",
       "   AMT_REQ_CREDIT_BUREAU_MON  AMT_REQ_CREDIT_BUREAU_QRT  \\\n",
       "0                        0.0                        0.0   \n",
       "1                        0.0                        0.0   \n",
       "2                        0.0                        0.0   \n",
       "3                        0.0                        0.0   \n",
       "4                        0.0                        1.0   \n",
       "\n",
       "   AMT_REQ_CREDIT_BUREAU_YEAR  \n",
       "0                         1.0  \n",
       "1                         0.0  \n",
       "2                         7.0  \n",
       "3                         1.0  \n",
       "4                         1.0  \n",
       "\n",
       "[5 rows x 122 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train = pd.read_csv('data/credit_example.csv')\n",
    "train_labels = train['TARGET']\n",
    "train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are several categorical columns in the dataset. The `FeatureSelector` handles these using one-hot encoding when using the feature importance based methods."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "train = train.drop(columns = ['TARGET'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Implementation\n",
    "\n",
    "The `FeatureSelector` has five functions for identifying columns to remove:\n",
    "\n",
    "* `identify_missing`\n",
    "* `identify_single_unique`\n",
    "* `identify_collinear`\n",
    "* `identify_zero_importance` \n",
    "* `identify_low_importance`\n",
    "\n",
    "These methods find the features to drop according to specified criteria. The identified features are stored in the `ops` attribute (a Python dictionary) of the `FeatureSelector`. We can remove the identified features manually or use the `remove` function in the  `FeatureSelector` for actually removing the features. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create the Instance\n",
    "\n",
    "The `FeatureSelector` only requires a dataset with observations in the rows and features in the columns (standard structured data). We are working with a classifified machine learning problem so we also pass in training labels."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "fs = FeatureSelector(data = train, labels = train_labels)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. Missing Values\n",
    "\n",
    "The first feature selection method is straightforward: find any columns with a missing fraction greater than a specified threshold. For this example we will use a threhold of 0.6 which corresponds to finding features with more than 60% missing values. (This method does not one-hot encode the features first)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "17 features with greater than 0.60 missing values.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "fs.identify_missing(missing_threshold=0.6)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The features identified for removal can be accessed through the `ops` dictionary of the `FeatureSelector` object. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['OWN_CAR_AGE',\n",
       " 'YEARS_BUILD_AVG',\n",
       " 'COMMONAREA_AVG',\n",
       " 'FLOORSMIN_AVG',\n",
       " 'LIVINGAPARTMENTS_AVG',\n",
       " 'NONLIVINGAPARTMENTS_AVG',\n",
       " 'YEARS_BUILD_MODE',\n",
       " 'COMMONAREA_MODE',\n",
       " 'FLOORSMIN_MODE',\n",
       " 'LIVINGAPARTMENTS_MODE']"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "missing_features = fs.ops['missing']\n",
    "missing_features[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also plot a histogram of the missing column fraction for all columns in the dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 700x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fs.plot_missing()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For detailed information on the missing fractions, we can access the `missing_stats` attribute, a dataframe of the missing fractions for all features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>missing_fraction</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>COMMONAREA_AVG</th>\n",
       "      <td>0.6953</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>COMMONAREA_MODE</th>\n",
       "      <td>0.6953</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>COMMONAREA_MEDI</th>\n",
       "      <td>0.6953</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NONLIVINGAPARTMENTS_AVG</th>\n",
       "      <td>0.6945</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NONLIVINGAPARTMENTS_MODE</th>\n",
       "      <td>0.6945</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NONLIVINGAPARTMENTS_MEDI</th>\n",
       "      <td>0.6945</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LIVINGAPARTMENTS_MEDI</th>\n",
       "      <td>0.6846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LIVINGAPARTMENTS_AVG</th>\n",
       "      <td>0.6846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LIVINGAPARTMENTS_MODE</th>\n",
       "      <td>0.6846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FONDKAPREMONT_MODE</th>\n",
       "      <td>0.6820</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          missing_fraction\n",
       "COMMONAREA_AVG                      0.6953\n",
       "COMMONAREA_MODE                     0.6953\n",
       "COMMONAREA_MEDI                     0.6953\n",
       "NONLIVINGAPARTMENTS_AVG             0.6945\n",
       "NONLIVINGAPARTMENTS_MODE            0.6945\n",
       "NONLIVINGAPARTMENTS_MEDI            0.6945\n",
       "LIVINGAPARTMENTS_MEDI               0.6846\n",
       "LIVINGAPARTMENTS_AVG                0.6846\n",
       "LIVINGAPARTMENTS_MODE               0.6846\n",
       "FONDKAPREMONT_MODE                  0.6820"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fs.missing_stats.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. Single Unique Value\n",
    "\n",
    "The next method is straightforward: find any features that have only a single unique value. (This does not one-hot encode the features)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4 features with a single unique value.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "fs.identify_single_unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['FLAG_MOBIL', 'FLAG_DOCUMENT_10', 'FLAG_DOCUMENT_12', 'FLAG_DOCUMENT_17']"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "single_unique = fs.ops['single_unique']\n",
    "single_unique"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can plot a histogram of the number of unique values in each feature of the dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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DpqRIrcTPLCAgQAEBASm2S+4zS0nnzp0tp8W9vLwk/X06/PDhw6pUqZKqV6+e7OOCg4O1YsUKhYSEKCIiwjKWKHHbTOspHhJf04IFCx56dDXxPbKzs9O0adP0zjvvKDQ0VJMnT9bkyZP19NNP68UXX1S3bt0eK5ilpWHDhunSpUv69ddf9e233+rbb79V/vz55eXlpQ4dOqQqxD9qv0r8HkjcLhPfl8TvgX972LQvKe0X0pNtBw/7/vj3P4z++VwwH4IZMk316tXVu3dvzZs3TyNGjLCa2Tu1HvajLsnq1IwtUpprKvEL8t8hw93dXeXKlUvx+ZL7UnzU6cvk+k3pyzVxva3TTuTPn1937tx55KTA0dHRkpTsvFK2/AA86rGP+/qS214Sl3l6ej50+oXHGYfj4+Ojzz77TIGBgRozZoycnJwsg/4Tr9z8t48++kjLly+Xvb29nn/+ebVs2VIuLi6qXr26du7cqblz56a6/+T8+7TqP5dVr15dZcqUSfGx9vb2lv+vXLmyNm3apL1792rHjh3au3evwsPDtWzZMv3www8aM2aMunbtalOtT6JQoUKaP3++fvvtN23dulW//vqrTp48qS1btmjLli1q27btI8dWpna/Svw+iIuLs1qeUvvkpLS/P+l2kBWnmEHyCGbIVO+884527Nihs2fPpnhKM/FLMrkf1Vu3bqVrfYlSmqrg4sWLkv53RCfxdEbDhg317rvvpls9iQEipTFH58+flySbx2CVLl1av/32my5evPjQU62J4/LSemLYxOdLfJ//LbnpGRK3l+SCSHLbS+Jn1rZt2zSbV8zJyUmtW7fW8uXLtXPnTvn6+mrdunXKnTu32rdvn6T9vn37tHz5cpUsWVLz5s1T+fLlrdan9iKHxB/75F777du3kyxLfO2NGjXSoEGDUtWH9HdQa9SokeX04MWLF7Vo0SItWrRIkyZNUufOna3CXEZ64YUX9MILL2jIkCH666+/tGnTJo0fP17r16+Xn5+f3N3dU3xsaverxIseEvf75K7klh7vKKuUdtsBsjbGmCFT5cmTRwEBAcqVK5cWLlyoP/74I0mbxEHS169fT7IupXmW0tq+ffuSBMP4+HgFBgZK+vtoi/S/cS4///xzskFy27ZtatOmjT755BOb6knsZ/Pmzcmu37Rpk6S/x7rZIvGK2S1btqTYJjw8XGFhYXJwcEjzK2zr1asn6e+rI/8dNhISEpKdxuJxt5fE9zKlKTGWLl2q9u3ba+bMmY9Ve+KRsS1btuj48eO6cOGCmjdvnuxppcS6fHx8kvwYP3jwwDIW7FFHiBMHnyc3YD651564fezZsyfZoztbtmxRmzZtNHbsWEl/7wctW7bUmDFjrNqVLFnScjFNVFSUoqKiHlpnWrt586Y6duyYJPQWKlRI3bp1U4MGDST9LyildEQscVtIaXtPvK9p4vvm7u6ufPny6cSJE8lubzt27His15FW2wGyNoIZMp2Hh4f8/PyUkJCQ7JioxMvSt23bZnXk5PTp0/r2228zpMZz585p0qRJlh+vhIQEBQQEKDw8XB4eHpYxQ56enpZ5uiZNmmQ1SPfcuXMaO3asQkNDH3qaMzVatWqlEiVKKCgoSN98843Vj+qePXs0d+5c2dvbq3v37jb14+fnJycnJ61Zs0YLFy5MEo7+/PNPy1xI3bt3V4ECBWzq79+qVaumOnXq6OzZs5o0aZLlB8kwDH3xxRc6c+ZMksckTpy7Zs0a/fXXX5blQUFBVnO7JfL19VWJEiUUGBioBQsWWL2Xx48f15dffqmQkJDHujgjsXZXV1ft2bNH69atk5TyaczE06R79+61uhji7t27Gj16tOUfLI8aqJ24r2zZssXqKE5ISIjmzJmTpH2DBg3k6uqq48ePa/LkyZZTc5J09uxZjRs3TqGhoZaQ4OrqqoiICK1ZsybJJMbbtm3T3bt3VbZs2ccaL5kWnJ2dFRcXp5CQkCRjO8+fP68jR47I3t7eMgje3t7eMvzgzp07lratW7dWsWLFtHfvXn377bdW28KuXbs0f/58OTg4WE7V5suXT507d1ZcXJxGjRpl9dnt3r3bMmdhak/np9V2gKyNU5kwhSFDhmjnzp3Jzlnk6empqlWr6r///a/atm0rT09P3b17VwcPHlTjxo11+PDhdK+vevXqWrhwoXbt2iVXV1edPn1a4eHheuaZZzR58mRLOzs7O33xxRfy8/PTggULtHHjRlWpUkUxMTEKDg5WXFycWrRooZ49e9pUj5OTk6ZNm6Z+/frpyy+/1Jo1a1S5cmVduXLF8iM0atSoJPNoPa7SpUtrwoQJGj58uAICAjR37lxVqVJFTk5Ounz5sk6cOKEHDx7I29tb//d//2dTXymZOHGievXqpYULF2rnzp16/vnnFRoaqj/++EPPPvtsktNIvr6+mjlzpiIiItSiRQvVrl1bkZGROnLkiF5++WXLWK9E/3wvJ0yYYLmbwK1bt3T48GEZhqHXXnvNMlXB4+jUqZMCAgK0bNkylSxZ0nLk5t98fX319ddfKyQkRM2aNVP16tUVGxurw4cPKyoqSpUqVVJoaOgjL+Zo0KCBXnjhBf32229q06aNPD09FRUVpeDgYL344ouWIy6J7Ozs9OWXX8rPz0/z5s3T+vXrk2yvrVq1Urdu3ST9fWp86NChmjRpkrp166YaNWqoRIkSunz5so4dO6bcuXPro48+euz3KS188skneu211zR27FgtW7ZMFStW1J07dxQcHKzY2Fi9/fbbVoP6y5YtqzNnzujVV19VuXLlNHnyZOXLl0/Tpk1T//79NWXKFP3444+qXLmyLl++rKNHj8rBwUEffvihqlatanmed955R/v379fOnTvVrFkz1axZU5GRkTp06JDKlCmjiIiIVI91TavtAFkbR8xgCnnz5tX48eOTHRCbK1cuLViwQH5+fipUqJB+/vln/fnnnxo8eLBmzJiRIVcXNW3aVDNnzlS+fPm0c+dORUdHq2vXrlq5cqXlCshE5cuX15o1a9S3b1/ly5dPv/76q0JCQlS1alUFBARo6tSpaTL+pmbNmlq9erVeeeUV3b9/X9u3b9fFixfl6+urZcuWWc2RZIuWLVtq7dq16tu3r5ydnXX06FFt377dMsP4tGnT9PXXX1vN4ZSWSpYsqZUrV6pnz566d++eduzYobx582rGjBmWU53/lD9/fi1btkwdO3aUvb29du/erejoaI0ZM0ajR49Oto+aNWtqzZo16tatmwzD0J49exQRESFPT099/fXXGjVq1BPV3q5dO+XOnVtxcXHq1KlTittqoUKFtGLFCnXs2FGOjo7as2ePzpw5o+rVq2vatGlauHCh7OzstGfPHj148CDF/uzt7bVw4UL16tVLBQoU0J49e3TlyhUNGTJE06ZNS7b/ihUrau3aterTp0+S7XXixImaMmWK1fbat29fTZkyRXXq1NGZM2e0fft2Xbp0SW3atNHKlSstV6FmtJo1a+o///mPWrRooVu3bmn79u367bffVLt2bX399ddJxtAFBASoSpUqCg8PV1BQkOVofO3atbVmzRp16dLFsl8lvr5ly5ZZQmqiAgUK6D//+Y/8/f3l6OioHTt26OrVqxo6dKhlnGlqb7aeVtsBsjY7I62vvwaADDJ8+HCtXr1aY8eO5YbgyBT//e9/9dxzzyV71W7iXQcGDBiQrhcDIXvhiBkAAE+of//+aty4scLCwqyWR0REaNGiRbKzs3ui0+DIuRhjBgDAE+rbt68mTpyo9u3bq2bNmipatKiuX7+uI0eOKC4uTm+//fZDp+gA/o1gBgDAE+rTp48qVqyo77//XqdOndKRI0f01FNPqUGDBurRo4flll5AajHGDAAAwCQYYwYAAGASBDMAAACTyJFjzGrXrq3Y2FjLfeIAAADSy7Vr1+To6Kjg4OBHts2Rwez+/fvJ3uQXAAAgrT148CDZ+9EmJ0cGsxIlSkiStm/fnsmVAACA7O6ll15KdVvGmAEAAJgEwQwAAMAkCGYAAAAmQTADAAAwCYIZAACASZgymN24cUPNmzfXgQMHLMuOHTumLl26yMPDQ97e3lqxYoXVY1avXq3mzZurRo0a6tixo44cOZLRZQMAANjEdMHs0KFD6tq1qyIiIizLbt++rX79+qlDhw46ePCgxo0bp4CAAB0/flySdODAAX322WeaMGGCDh48qHbt2unNN9/UvXv3MutlAAAAPDZTBbPVq1frvffe07vvvmu1fOvWrSpcuLB69OghBwcH1a9fX23bttXSpUslSStWrFDr1q1Vq1Yt5c6dW71795azs7M2bdqUGS8DAADgiZgqmHl5eSkwMFC+vr5Wy0NDQ+Xq6mq1zMXFRSEhIZKksLCwh64HAADICkw1839K966Mjo6Wk5OT1bK8efPq7t27qVoPAACQFZgqmKXEyclJd+7csVoWExOj/PnzW9bHxMQkWe/s7JxhNQIAYBYRERGKjIzMsP6KFSumMmXKZFh/2VmWCGaurq769ddfrZaFhYWpUqVKkqRKlSopNDQ0yfrGjRtnWI0AAJhBRESE3Co/r5h7GXfWKK9TPp0OOUU4SwNZIpg1b95ckydP1sKFC9WjRw8dOnRI69ev18yZMyVJnTt31sCBA9WqVSvVqlVLS5cu1fXr19W8efNMrhwAgIwVGRmpmHt3VbTNUOUuWjrd+4u7fl7XN0xRZGSkKYJZ69at1b9/f7Vr1y6zS3kiWSKYOTs7a/78+Ro3bpy++uorFSlSRKNHj1a9evUkSfXr19fHH3+sMWPG6MqVK3JxcdGcOXNUuHDhTK4cAIDMkbtoaeV5xiWzy8hwGzduzOwSbGKqqzL/6fTp0/L09LT87e7urmXLlunw4cPatm2bOnbsaNW+ffv22rx5s44cOaIVK1aoevXqGV0yAABIhQsXLsjNzU0rVqyQt7e3atWqJX9/f12+fFmrVq2St7e3VftevXpp+vTpkqThw4fro48+0oABA+Th4aGXXnpJ3333naWtt7e3Vq1aJUmKiorS8OHDVatWLXl5eemHH36Qm5ubLly4IElyc3Ozmsz+332fPHlSvXr1Up06ddSiRQstXLhQhmGk2/siZZEjZllRRg+8TC8M6AQApJddu3ZpzZo1io2Nlb+/v2bOnKkaNWo88nGrVq3S7NmzNWPGDK1cuVKffvqpfHx89PTTT1u1++STT3T+/Hlt2bJFuXLlSjJP6sNcuXJFfn5+evfddzV//nydO3dOb731lvLmzatu3bo99mtNLYJZOsiMgZfphQGdAID08sYbb6hQoUKS/j7SdeTIkVQFM09PTzVs2FCS1KlTJ3388ceKiIiwCmaxsbH66aefNHv2bBUrVkySNGLECLVv3z5Vta1bt04VK1ZUjx49JP09P2rfvn21ZMkSgllWk9EDL9OL2QZ0AgCyl8TAJEkODg6pPk34z3lPc+fOLUlKSEiwavPXX38pLi5Ozz77rGVZ+fLlU13bxYsXdfLkSdWuXduyLCEhQfb29ql+jidBMEtHOXXgJQAATypXrlyKjY21Wnbz5s3Hfp6iRYsqX758On/+vCpUqCBJunz5cpK+4uLiku3nmWeekaenp+bNm2e1Pjo6+rFreRwEMwAAsqG46+ezZD8VK1ZUZGSk9u/fL09PT61bt05nzpx57Oexs7NT9+7dNW3aNLm6uqpgwYKaNGlSkr62bNmievXq6dKlS1q5cqVlXdu2bTVnzhytW7dOvr6+unHjhgYNGqTixYtrxowZNr/OlBDMAADIRooVK6a8Tvl0fcOUDOszr1M+q9OStnB3d9ebb76p4cOHKzo6Ws2aNZOPj88TPdeQIUP01VdfqUuXLnJwcEhyL+6PP/5YAQEBqlu3rsqVK6fOnTtr6dKlkqSSJUtq7ty5+vzzzzV27FjZ29uradOmGjVqlM2v8WHsjPS+7tOEXnrpJUnS9u3b0+X5Dx8+rFq1aukZvy+z9KnM+5fDdHnREB06dEg1a9bM7HIAAKnELZmSd+HCBb300kvavn27SpUqlWH9Pk7u4IgZAADZTJkyZbJEUEJSpp1gFgAAIKfhiBkAAMgRSpUqpdOnT2d2GQ/FETMAAACTIJgBAACYBMGEKECkAAAgAElEQVQMAADAJAhmAAAAJkEwAwAAMAmCGQAAgEkQzAAAAEyCYAYAAGASBDMAAACTIJgBAACYBMEMAADAJAhmAAAAJkEwAwAAMAmCGQAAgEkQzAAAAEyCYAYAAGASBDMAAACTIJgBAACYBMEMAADAJAhmAAAAJkEwAwAAMAmCGQAAgEkQzAAAAEyCYAYAAGASBDMAAACTIJgBAACYBMEMAADAJAhmAAAAJkEwAwAAMAmCGQAAgEkQzAAAAEyCYAYAAGASBDMAAACTIJgBAACYBMEMAADAJAhmAAAAJkEwAwAAMAmCGQAAgEkQzAAAAEyCYAYAAGASBDMAAACTIJgBAACYBMEMAADAJAhmAAAAJkEwAwAAMAmCGQAAgEkQzAAAAEyCYAYAAGASBDMAAACTIJgBAACYBMEMAADAJAhmAAAAJkEwAwAAMIksFcxOnjypHj16qHbt2vLy8tLYsWMVGxsrSdq9e7fatm2rGjVqqFWrVtq5c2cmVwsAAPB4skwwS0hIUP/+/eXj46OgoCCtXLlSv/zyi+bMmaPw8HANGjRI77zzjoKDgzVo0CANGTJEV65cyeyyAQAAUi3LBLPbt2/r2rVrSkhIkGEYkqRcuXLJyclJq1evVu3atdWsWTM5ODjI19dXderU0fLlyzO5agAAgNTLMsHM2dlZvXv31sSJE+Xu7q4mTZqoXLly6t27t8LCwuTq6mrV3sXFRSEhIZlULQAAwOPLMsEsISFBefPm1YcffqijR49qw4YNOnPmjL766itFR0fLycnJqn3evHl19+7dTKoWAADg8WWZYBYYGKgtW7bo1VdflaOjoypVqqSBAwfq+++/l5OTk2JiYqzax8TEKH/+/JlULQAAwOPLMsHszz//tFyBmcjBwUG5c+eWq6urQkNDrdaFhYWpUqVKGVkiAACATbJMMPPy8tK1a9c0a9YsxcfH6/z58/rmm2/Utm1btWvXTkFBQdq0aZMePHigTZs2KSgoSO3bt8/ssgEAAFLNIbMLSC0XFxfNnj1bX375pebOnauCBQuqXbt2GjhwoBwdHfX111/r888/16hRo1SyZElNnz5d5cuXz+yyAQAAUi3LBDNJatCggRo0aJDsukaNGqlRo0YZXBEAAEDayTKnMgEAALI7ghkAAIBJEMwAAABMgmAGAABgEgQzAAAAkyCYAQAAmATBDAAAwCQIZgAAACZBMAMAADAJghkAAIBJEMwAAABMgmAGAABgEgQzAAAAkyCYAQAAmATBDAAAwCQIZgAAACZBMAMAADAJghkAAIBJEMwAAABMgmAGAABgEgQzAAAAkyCYAQAAmATBDAAAwCQIZgAAACZBMAMAADAJghkAAIBJEMwAAABMgmAGAABgEgQzAAAAkyCYAQAAmATBDAAAwCQIZgAAACZBMAMAADAJghkAAIBJEMwAAABMgmAGAABgEgQzAAAAkyCYAQAAmATBDAAAwCQIZgAAACZBMAMAADAJghkAAIBJEMwAAABMgmAGAABgEgQzAAAAkyCYAQAAmATBDAAAwCQIZgAAACZBMAMAADAJghkAAIBJEMwAAABMgmAGAABgEgQzAAAAkyCYAQAAmATBDAAAwCQIZgAAACZhUzDr27evNm3apNjY2LSqBwAAIMdysOXBx48f1969e1WgQAG1bt1aHTt2VLVq1dKqNgAAgBzFpiNmv/76q6ZMmSIPDw+tWLFCXbt2la+vr+bNm6dr166lVY0AAAA5gk3BzNHRUb6+vvr222+1e/duvf/++7K3t9fkyZP14osvqn///tq6davi4uLSql4AAIBsy6ZTmf9UrFgx9enTR3369FF4eLhmzZqlNWvWaM+ePXJ2dlanTp302muvqXjx4mnVJQAAQLaSZsFMks6ePau1a9dqy5YtOnv2rBwcHOTt7S1JWrhwoZYtW6aZM2eqTp06adktAABAtmBzMLtx44Y2btyotWvX6uTJkzIMQ5UqVdLw4cPVrl07FSlSRJJ0/vx5devWTWPGjNHGjRttLhwAACC7sSmYDRgwQL/88osePHigggUL6pVXXlGnTp2SvTKzdOnSqlu3rvbs2fPE/d26dUvjx4/X7t27lZCQoDp16mjMmDEqUaKEjh07prFjxyosLEzOzs5688031aVLF1teHgAAQIayKZjt3r1bdevWVadOneTj46M8efI8tH29evXUoEGDJ+5v0KBBeuqppxQYGKhcuXJpxIgR+vDDDzVp0iT169dPgwcPVteuXXXw4EENHDhQbm5uTN8BAACyDJuCWWBgoEqVKpVkeWxsrBwdHZMs79q16xP39d///lfHjh2zzJsmSZ999pmuXbumrVu3qnDhwurRo4ckqX79+mrbtq2WLl1KMAMAAFmGTdNllCpVSj///LO6dOmiy5cvW5Z/9tln6tChg44cOWJzgYmOHz8uFxcX/fDDD2revLm8vLw0ceJEFS9eXKGhoXJ1dbVq7+LiopCQkDTrHwAAIL3ZFMz279+vAQMG6Ny5c4qKirIsr1ixoiIjI+Xn56fffvvN5iIl6fbt2zp9+rTCw8O1evVqrVmzRleuXNEHH3yg6OhoOTk5WbXPmzev7t69myZ9AwAAZASbgtmsWbNUpkwZbd68WS4uLpblvXv31oYNG1SyZEl99dVXNhcpyXJqdNSoUSpQoICKFSumIUOGaPfu3TIMQzExMVbtY2JilD9//jTpGwAAICPYFMxOnTqlV1991TIlxj8VLlxYr7zyio4fP25LFxYuLi5KSEiwuotAQkKCJOn5559XaGioVfuwsDBVqlQpTfoGAADICDYFs/j4eMXGxqa43s7OTvfu3bOlC4sGDRqodOnSGjlypKKjo3Xjxg1NnTpVzZo1U5s2bRQZGamFCxcqLi5O+/fv1/r169WpU6c06RsAACAj2BTMKleurA0bNujBgwdJ1sXHx2vTpk1pdtQqd+7cWrx4sezt7eXj4yMfHx8988wzGj9+vJydnTV//nxt3rxZnp6eGj16tEaPHq169eqlSd8AAAAZwabpMl599VX93//9n/r27Ss/Pz9VqFBBdnZ2Onv2rJYsWaITJ05o4sSJaVWrnn76aU2dOjXZde7u7lq2bFma9QUAAJDRbApmvr6+CgsL0+zZsxUUFJRkfb9+/dSuXTtbugAAAMgxbL5X5uDBg9WhQwdt375dFy9eVFxcnEqVKqWXXnpJFSpUSIsaAQAAcgSbg5kklSlTRv7+/mnxVAAAADlWmgSziIgIXbt2zTJ9xb/VqVMnLboBAADI1mwKZpGRkXr33XcVHBz80HanTp2ypRsAAIAcwaZgNmXKFB08eFD169eXu7t7sjcuBwAAQOrYFMx27dql1q1ba8qUKWlVDwAAQI5l0wSzd+/eZRJXAACANGJTMHNxcdHZs2fTqhYAAIAczaZg1r9/fy1fvlwnTpxIq3oAAAByLJvGmAUFBalw4cJ65ZVXVLZsWRUvXlx2dnZWbezs7LRo0SKbigQAAMgJbApmS5Yssfx/eHi4wsPDk7T5d1ADAABA8mwKZiEhIWlVBwAAQI5n0xgzAAAApB2bg1lsbKzmzZunbt26ycvLS8HBwTp58qQ+++wzXb9+PS1qBAAAyBFsOpV5//59+fn56ejRo8qTJ49iY2MVFxenyMhILV26VL/88ov+85//qGjRomlVLwAAQLZl0xGzWbNm6dixYxo3bpy2b98uwzAkST4+Pvr444914cIFzZ49O00KBQAAyO5sCmabNm1S+/bt1alTJ9nb21uW29nZqXv37urYsaN27dpla40AAAA5gk3B7NKlS6pRo0aK66tVq6bLly/b0gUAAECOYVMwK1iw4EMH+EdERKhgwYK2dAEAAJBj2BTM6tWrpxUrVujOnTtJ1p0/f17Lli1TnTp1bOkCAAAgx7DpqsxBgwapc+fOat++vZo0aSI7Oztt375d27dv16pVqxQfH68BAwakVa0AAADZmk1HzMqXL68FCxYoX758+v7772UYhpYsWaIlS5bI2dlZs2bNUuXKldOqVgAAgGzNpiNm0t8D/Dds2KDTp0/r7NmzSkhIUKlSpVS1alXlysWNBQAAAFLL5mCWyM3NTW5ubmn1dAAAADmOTcFsxowZj2xjZ2engQMH2tINAABAjpBuwczOzk6GYRDMAAAAUsmmYPbdd98lWRYfH69r165p7dq1unLlir799ltbugAAAMgxbApmdevWTXFd27Zt1bNnT82fP1+jR4+2pRsAAIAcId0um7Szs1Pr1q21efPm9OoCAAAgW0nX+Szu3buX7F0BAAAAkFSaTZfxT7GxsTpx4oQWLVqkihUrpkcXAAAA2Y5Nwaxy5cqys7NLcb1hGBo+fLgtXQAAAOQYNgWzlG5Q7uDgoBIlSqhTp04PvUAAAAAA/2NTMFu8eHFa1QEAAJDjcTNLAAAAk7DpiNmIESMe+zF2dnYaP368Ld0CAABkSzYFs82bNys+Pl5xcXEyDEOSZG9vr/j4eEn/uy3TPxHMAAAAkmdTMFu3bp1ee+01ubq66u2335arq6ty586t8+fPa+7cuVq/fr2mTZsmFxeXtKoXAAAg27IpmI0fP16lSpXSrFmzrKbNKFu2rD777DPduHFDixcv1ty5c20uFAAAILuzafD//v371bJlyxTnMvPy8lJwcLAtXQAAAOQYNgWzvHnz6tKlSymuDwsLU4ECBWzpAgAAIMewKZg1aNBAS5cu1S+//JJk3ebNm7V8+XI1a9bMli4AAAByDJvGmA0ZMkS//PKL3njjDbm4uKhcuXKS/j5SFh4erjJlymjIkCFpUScAAEC2Z9MRs9KlS2v16tXy9fXVxYsXFRgYqMDAQN24cUPdunXTDz/8oMKFC6dVrQAAANmaTUfMJOm5557TlClTZBiGbt68KUkqUqSIzYUBAADkNGlyS6b79+/r119/VWBgoOLi4vTXX3/p2rVrafHUAAAAOYbNwWzr1q168cUX9cYbb2jMmDH6448/dPToUXl7e2vBggVpUSMAAECOYFMwO3z4sN59910VLlxY/fv3tywvUaKEnnvuOU2aNEnbtm2zuUgAAICcwKZg9s0336hkyZL68ccf9dprr1nui1m5cmX9+OOPKl++vBYuXJgWdQIAAGR7NgWzo0ePqmPHjnJyckoy+3+BAgXUpUsXhYWF2VQgAABATmFTMIuNjdVTTz2V4noHBwfFxMTY0gUAAECOYVMwK1++vIKCglJcv2PHDpUtW9aWLgAAAHIMm4JZp06dtHnzZi1YsEDR0dGSJDs7O926dUtjxozR/v371b59+zQpFAAAILuzaYLZnj176siRI5o4caImTZokOzs7vf3224qOjpZhGPLy8pKfn19a1QoAAJCt2RTM7Ozs9MUXX6h58+bauHGjwsPDFR8fLw8PD/n4+Khjx47KlStN5rAFAADI9mwKZt9//73q16+vVq1aqVWrVmlVEwAAQI5k0+Gszz//XOvXr0+rWgAAAHI0m4JZrly55OzsnFa1AAAA5Gg2BbO+ffvq22+/1c8//6yEhIS0qgkAACBHsmmM2dGjRxUVFaV+/frJ0dFRzs7Osre3t2pjZ2fH/TIBAABSwaZg9vvvv6tw4cIqXLiwZVni/TJT+hsAAADJsymY7dixI63qAAAAyPEea4zZ888/z1WYAAAA6eSxgllypyVv3ryp559/Xvv27Uuzoh4lPj5evXr10vDhwy3Ldu/erbZt26pGjRpq1aqVdu7cmWH1AAAApIU0mZY/o8eRzZgxQ8HBwZa/w8PDNWjQIL3zzjsKDg7WoEGDNGTIEF25ciVD6wIAALBFlrtf0r59+7R161a1aNHCsmz16tWqXbu2mjVrJgcHB/n6+qpOnTpavnx5JlYKAADweLJUMLt+/bpGjRqlKVOmyMnJybI8LCxMrq6uVm1dXFwUEhKS0SUCAAA8sSwTzBISEvT+++/L399flStXtloXHR1tFdQkKW/evLp7925GlggAAGCTLBPMZs+eLUdHR/Xq1SvJOicnJ8XExFgti4mJUf78+TOqPAAAAJs99jxmgYGBOnfunOXvmJgY2dnZae3atTp06FCS9nZ2dho4cKBtVUpau3atrl69qtq1a1v6laRt27apR48eOnnypFX7sLAwVa1a1eZ+AQAAMspjB7OtW7dq69atSZavWbMm2fZpFcw2b95s9XfiVBkTJkzQmTNntGDBAm3atEktWrTQ1q1bFRQUpFGjRtncLwAAQEZ5rGAWEBCQXnXYpGLFivr666/1+eefa9SoUSpZsqSmT5+u8uXLZ3ZpAAAAqfZYwezll19Orzoe24QJE6z+btSokRo1apRJ1QAAANguywz+BwAAyO4IZgAAACZBMAMAADAJghkAAIBJEMwAAABMgmAGAABgEgQzAAAAkyCYAQAAmATBDAAAwCQIZgAAACZBMAMAADAJghkAAIBJEMwAAABMgmAGAABgEgQzAAAAkyCYAQAAmATBDAAAwCQIZgAAACZBMAMAADAJghkAAIBJEMwAAABMgmAGAABgEgQzAAAAkyCYAQAAmATBDAAAwCQIZgAAACZBMAMAADAJghkAAIBJEMwAAABMgmAGAABgEgQzAAAAkyCYAQAAmATBDAAAwCQIZgAAACZBMAMAADAJghkAAIBJEMwAAABMgmAGAABgEgQzAAAAkyCYAQAAmATBDAAAwCQIZgAAACZBMAMAADAJghkAAIBJEMwAAABMgmAGAABgEgQzAAAAkyCYAQAAmATBDAAAwCQIZgAAACZBMAMAADAJghkAAIBJEMwAAABMgmAGAABgEgQzAAAAkyCYAQAAmATBDAAAwCQIZgAAACZBMAMAADAJghkAAIBJEMwAAABMgmAGAABgEgQzAAAAk8hSwSwkJET+/v6qW7euGjZsqGHDhunGjRuSpGPHjqlLly7y8PCQt7e3VqxYkcnVAgAAPJ4sE8xiYmL0+uuvy8PDQ7/88os2bNigW7duaeTIkbp9+7b69eunDh066ODBgxo3bpwCAgJ0/PjxzC4bAAAg1bJMMLt06ZIqV66sgQMHytHRUc7OzuratasOHjyorVu3qnDhwurRo4ccHBxUv359tW3bVkuXLs3ssgEAAFItywSzChUqaO7cubK3t7cs27Jli6pUqaLQ0FC5urpatXdxcVFISEhGlwkAAPDEskww+yfDMDR16lTt3LlTo0aNUnR0tJycnKza5M2bV3fv3s2kCgEAAB6fQ2YX8LiioqI0YsQInTx5UkuWLJGbm5ucnJx0584dq3YxMTHKnz9/JlUJAADw+LLUEbOIiAh16tRJUVFRWrlypdzc3CRJrq6uCg0NtWobFhamSpUqZUaZAAAATyTLBLPbt2/Lz89PNWvW1Lx581SkSBHLuubNmysyMlILFy5UXFyc9u/fr/Xr16tTp06ZWDEAAMDjyTKnMletWqVLly7pp59+0ubNm63WHTlyRPPnz9e4ceP01VdfqUiRIho9erTq1auXSdUCAAA8viwTzPz9/eXv75/iend3dy1btiwDKwIAAEhbWeZUJgAAQHZHMAMAADAJghkAAIBJEMwAAABMgmAGAABgEgQzAAAAkyCYAQAAmATBDAAAwCQIZgAAACZBMAMAADAJghkAAIBJEMwAAABMgmAGAABgEgQzAAAAkyCYAQAAmATBDAAAwCQIZgAAACZBMAMAADAJghkAAIBJEMwAAABMgmAGAABgEgQzAAAAkyCYAQAAmATBDAAAwCQIZgAAACZBMAMAADAJghkAAIBJEMwAAABMgmAGAABgEgQzAAAAk3DI7AJgfqdOncrsEmxSrFgxlSlTJrPLAADgkQhmSFF81E3Jzk49e/bM7FJsktcpn06HnCKcAQBMj2CGFCXcj5IMQ0XbDFXuoqUzu5wnEnf9vK5vmKLIyEiCGQDA9AhmeKTcRUsrzzMumV0GAADZHoP/AQAATIJgBgAAYBIEMwAAAJMgmAEAAJgEwQwAAMAkCGYAAAAmQTADAAAwCYIZAACASRDMAAAATIJgBgAAYBIEMwAAAJMgmAEAAJgEwQwAAMAkCGYAAAAm4ZDZBQAZ4dSpU5ldgk2KFSumMmXKZHYZAIB0RjBDthYfdVOys1PPnj0zuxSb5HXKp9MhpwhnAJDNEcyQrSXcj5IMQ0XbDFXuoqUzu5wnEnf9vK5vmKLIyEiCGQBkcwQz5Ai5i5ZWnmdcMrsMAAAeimAGIMNEREQoMjIys8uwCeP9AKQnghmADBERESG3ys8r5t7dzC7FJoz3A5CeCGYAMkRkZKRi7t1lvB8APATBDECGYrwfAKSMCWYBAABMgmAGAABgEgQzAAAAk2CMGQAAeGLZYRocyTxT4RDMAADAE8ku0+BI5pkKh2AGAACeSHaYBkcy11Q4BDMAAGATpsFJO9kqmF2/fl0ffvihgoKCZG9vr3bt2umDDz6Qg0O2epkAYJPsMibo/v37ypMnT2aXYROzjGuCeWSrxDJkyBA9/fTT+vnnnxUZGak333xTCxcu1Ouvv57ZpQGAKWSnMUGyyyUZCZldhU3MMq4J5pFtgtm5c+cUFBSkPXv2yMnJSaVLl9Zbb72lyZMnE8wA4P/LLmOC7v0RrNs/L8nSr8NM45pgHtkmmIWGhqpw4cJ6+umnLcsqVqyoS5cu6a+//lKhQoUysToAMJesPiYo7vp5SVn/dQD/ZmcYhpHZRaSFtWvXaurUqdq1a5dlWUREhJo3b67du3frmWeesSx3d3dXfHy8nn322XSpJTY2VhcuXJB9/sKyy5V1s2/Cg/tKuHcnS7+O7PAajIQHio++pRIlSsjR0TGzy3lisbGxunr1Kp9FJssOn4PEvm0W2WV7SvwsSpUqlS6fxZ9//il7e3udOHHikW2z7rv4L/ny5dO9e/esliX+nT9/fqvlefLkUWxsbLrV4ujoqAoVKqTb82es4pldQBrIDq+hSGYXYDNHR0cVKFAgs8tIA1n7s8g+n4PEvp35stf2lH6fhYODQ6oDX7YJZpUqVdKtW7cUGRmpYsWKSZLOnDmjZ555RgULFrRqGxwcnBklAgAAPFS2uVdmuXLlVKtWLY0fP15RUVE6f/68Zs6cqc6dO2d2aQAAAKmSbcaYSX9fbfTpp5/qwIEDypUrlzp06KD33ntP9vb2mV0aAADAI2WrYAYAAJCVZZtTmWZx/fp1vfXWW6pdu7Y8PT01btw4PXjwILPLyhZCQkLk7++vunXrqmHDhho2bJhu3LghSTp27Ji6dOkiDw8PeXt7a8WKFVaPXb16tZo3b64aNWqoY8eOOnLkiGVdfHy8Jk6cqAYNGsjDw0Nvvvmmrl69mqGvLSuKj49Xr169NHz4cMuy3bt3q23btqpRo4ZatWqlnTt3Wj1mzpw5aty4sWrUqKFevXrpjz/+sKy7e/euRowYIU9PT9WqVUvDhg1TdHR0hr2erOjWrVsaNmyYPD09VadOHb311luWbZd9ImOdPHlSPXr0UO3ateXl5aWxY8daLjJjv8gYN27cUPPmzXXgwAHLsvTcD9Lt995AmurZs6cxdOhQ4+7du0ZERITRunVrY86cOZldVpZ37949o2HDhsa0adOM+/fvGzdu3DDeeOMNo3///satW7eMunXrGkuWLDHi4uKMvXv3Gh4eHsaxY8cMwzCM/fv3Gx4eHkZwcLARGxtrLFiwwPD09DTu3r1rGIZhTJ8+3Wjbtq1x6dIl486dO8aQIUOMN954IzNfbpbw5ZdfGpUrVzY++OADwzAM4+zZs4a7u7sRGBhoxMXFGRs3bjSqVatmXL582TAMw1i1apXRqFEj4/fffzdiYmKMgIAAo3Xr1kZCQoJhGIYxfPhww8/Pz7h586YRGRlp9OzZ0xgzZkymvb6soGfPnsbAgQON27dvG3fu3DHefvtto1+/fuwTGSw+Pt5o2LChsWjRIiM+Pt74888/DR8fH2PGjBnsFxkkODjYaNasmeHq6mrs37/fMAwj3feD9Pq9J5ilofDwcMPV1dWywxmGYWzcuNFo2rRpJlaVPZw5c8bo27ev8eDBA8uybdu2GTVr1jR++OEHo0WLFlbtP/roI2PYsGGGYRjG0KFDjdGjR1utb9mypbFy5UrDMAyjcePGxrp16yzrrl27Zri5uRkRERHp9XKyvL179xq+vr7G4MGDLcHsiy++MPz9/a3a9e3b15g2bZphGIbRrVs345tvvrGsi42NNTw8PIx9+/YZd+/eNapUqWIcOnTIsv7o0aNGtWrVLF+SsHbixAnD3d3duHPnjmXZzZs3jd9//519IoPduHHDcHV1NRYsWGA8ePDA+PPPP41WrVoZ8+bNY7/IAKtWrTKaNm1qbNy40SqYped+kJ6/95zKTEOPuvsAnlyFChU0d+5cqws5tmzZoipVqig0NFSurq5W7V1cXBQSEiJJCgsLS3H9nTt3dPnyZav1xYoV01NPPaXTp0+n4yvKuq5fv65Ro0ZpypQpcnJysix/2Puc3PrcuXOrXLlyCgkJ0blz5xQXF2e1vmLFioqJiVF4eHj6vqAs6vjx43JxcdEPP/yg5s2by8vLSxMnTlTx4sXZJzKYs7OzevfurYkTJ8rd3V1NmjRRuXLl1Lt3b/aLDODl5aXAwED5+vpaLU/P/SA9f+8JZmkoOjra6odKkuXvu3ezwQ2DTcIwDE2dOlU7d+7UqFGjkn3f8+bNa3nPH7Y+caxGvnz5kqxnHEdSCQkJev/99+Xv76/KlStbrbPlc4iKipJk/TkktuVzSN7t27d1+vRphYeHa/Xq1VqzZo2uXLmiDz74gH0igyUkJChv3rz68MMPdfToUW3YsEFnzpzRV199xX6RAYoXLy4Hh6TTsqbnfpCev/cEszT0OHcfwJOJiorS4MGDtX79ei1ZskRubm5ycnJSTEyMVbuYmBjLe/6w9Yk70r8/t38+Hv8ze/ZsOTo6qlevXknW2fI5JH75/W4CSHYAAA2NSURBVPNzSPz/7DOreNpKnEV81KhRKlCggIoVK6YhQ4Zo9+7dMgyDfSIDBQYGasuWLXr11Vfl6OioSpUqaeDAgfr+++/ZLzJRev42pOfvPcEsDf3z7gOJUrr7AB5fRESEOnXqpKioKK1cuVJubm6SJFdXV4WGhlq1DQsLU6VKlST9/bmktP6pp57S008/rbCw/9fevcc0df5/AH8zQUWNOiy6TWUDL1WHWrSI08EQYYI0EgZY0aJucQuDQSRzglPBZS5zY25Sh8tQ5nURt8klLYKKkYGXhWUX3SLEC2rBiFEUZQq22Of3h+H8rAWUi1jzfb+S/tHnPOeczyk59J3nPOf0rLTs6tWrqKursxripvu/SVtWVgalUgmlUgm9Xg+9Xg+lUtnuv4PJZMKFCxcwevRouLq6wsHBweLvcO7cOemyDlkbOXIkzGYzTCaT1GY2mwEAY8eO5TnRjS5fvmz1M3/29vZwcHDgefEUPcnvhif6fd/pWWpkITIyUiQkJIj6+nrpLg2tVvu0y3rm1dXVCV9fX5GUlCTu3btnsez69etCqVSKrVu3CqPRKI4fPy5NnhVCSHfiHD9+XLrzxtPTU9y4cUMIIcQ333wjVCqVMBgM0p03Go2m24/xWZSYmChN/j979qwYP368yM/Pl+4+Gz9+vKisrBRC3J+I6+3tLcrLy6W7zwICAoTRaBRCCLFs2TKh0WhEbW2tqK2tFRqNRto2WTMajSIgIEDExcWJ//77T9TW1oqFCxeK2NhYnhPd7MyZM8Ld3V189913oqmpSRgMBqFSqcS6det4XnSzByf/P+nz4El93zOYdbGrV6+KuLg4MWXKFDF16lSxbt06izsJqWN++OEHMXr0aDFx4kShUCgsXkIIcfLkSaFWq4WHh4eYOXOm2Lt3r8X6ubm5YtasWUKhUIjw8HDx999/S8uMRqNITU0V3t7eYtKkSeL9998X165d69bje1Y9GMyEEKKkpETMmTNHKBQKERwcLIqLi6VlZrNZZGZmCj8/P6FQKERUVJT05SSEEPX19WLVqlVi2rRpwtPTUyQlJYnbt2936/E8a2pqasTSpUvF9OnThVKpFMuXLxc3b94UQvCc6G5Hjx4VERERYvLkycLX11d8/fXX4u7du0IInhfd6cFgJsSTPQ+e1Pc9n/xPREREZCM4x4yIiIjIRjCYEREREdkIBjMiIiIiG8FgRkRERGQjGMyIiIiIbASDGREREZGNYDAjIiIishEMZkTUbhs3boRcLkd2dnarfbKzsyGXy7Fx48YO7SMqKkr62S1blZGRAblcDq1W22a/O3fuwMPDAwEBAWjvoyOXLVsGuVyOmpqazpRKRM8IBjMisknR0dH48ssvn3YZbQoNDYW9vT30en2b/Q4cOIA7d+4gLCwMdnZ23VQdET2LGMyIyCZNnz4dISEhT7uMNjk7O8Pb2xsXL17EiRMnWu2Xk5ODHj16IDQ0tBurI6JnEYMZEVEnhIeHAwB0Ol2Lyy9fvoyysjL4+PhgyJAh3VkaET2DGMyIqNskJSXBw8MD1dXVWLp0Kby8vDBhwgTMmzcPR44csejb0hyz69evIzk5Ga+//jomTpyIqKgonDp1CiqVClFRURb7kcvlqK6utli/uroacrkcSUlJFu1XrlzB6tWr4ePjA3d3d8yYMQNr167FjRs3HnlMvr6+kMlk2LdvH5qamqyW5+XlwWw2SwGu2cWLF/Hxxx/Dz88P7u7uUCgUCA0Nxe7du9vc37FjxyCXy7Fp0yarZZGRkRg3bpxFm9lsxvbt2zFnzhxMmDABSqUS7777Lv766y+r9Xfu3Im33noLHh4e8PDwgFqtRm5u7iM/AyLqOvZPuwAi+t9iMpkwf/58jB07FvHx8airq8PWrVvx3nvvYd++fXjllVdaXK++vh5z587FpUuXEBERAblcjtLSUixcuBAODg54/vnnO1RPVVUVIiMjYTQaoVarMXToUFRUVCArKwslJSXIysqCk5NTq+vb29sjJCQEmZmZOHbsGHx8fCyW5+bmQiaTwdfXV2ozGAwICwuDo6Mj1Go1hgwZgitXruDnn3/GmjVrMGDAAMyePbtDx/MgIQSWLl2KAwcOIDAwEGq1GnV1ddi7dy80Gg3S0tLg7+8PANiyZQtSU1OhUqkwd+5cGI1GZGdnIzExEUajEXPnzu10PUT0aAxmRNStTCYT/Pz8sGbNGqlt2LBhWL58OXJycpCQkNDielu2bEFVVRVSUlIwf/58AMCCBQuQkpKCrKysDtfz6aefoqGhATk5OXBxcZHa33zzTbz99tvQarUWtbYkLCwMmZmZ0Ol0FsHsxIkTOH/+PJYsWQJ7+///d7tjxw7U19dj165dGDNmjNTu7++PkJAQ7Nu3r0uCWX5+Pvbv348VK1Zg8eLFUvuiRYsQFhaG5ORkeHt7o1evXvjll18wevRorF+/3uK41Go1KioqOl0LET0eXsokoieqpbsQ58yZY/F+/PjxAICrV6+2up2ioiL079/fauQmPj6+w7XdunULpaWlUCqV6NevH65fvy69xowZg+HDh+PgwYOP3M6IESPg4eGBoqIiNDQ0SO05OTkAYHUZc+XKlTh69KhFKDObzbh37x6A+4/X6Ap6vR52dnYICAiwODaj0YiAgADU1tbijz/+AAC8+OKLOHfuHLRaLSorKwEAffv2hV6vR3JycpfUQ0SPxhEzImq3Xr16AYAUJFrSvKy574OcnZ0t3vfs2RPA/XDSmurqari6ulqMPAHAoEGDMGjQoMcr/CEXLlyA2WxGcXExXnvttVb7NTY2onfv3m1uKzw8HCtXrsShQ4egUqlgNBpRUFAApVIJV1dXi752dnYwGo3QarUoLy/HpUuXYDAY0NjYCKDtz6E9zp8/DyEE/Pz8Wu1z6dIlAMCqVasQExOD9PR0pKen44UXXsC0adMQGBiIN954o0vqIaJHYzAjonYbOHAggPvzvlpTV1cHABgwYIDVsq5+ltejQlOzh4NkcwCaNWsW5s2b1+p6D4fBlgQFBeGzzz6DTqeDSqXC4cOHUVdXZzVaBgBHjx5FdHQ0HB0d4eXlhZkzZ2LUqFFQKBSYMWPGYx1LS1o6vv79+yMtLa3Vddzc3ADcH/UrKCjA77//jtLSUvz222/Izc1FdnY2goKCsGHDhg7XRUSPj8GMiNqt+RJcW3OPysvLAQBjx47tkn26ubnh4sWLMBqN0ggbcH80q6amBkOHDpXaevToAQC4e/euxTYevlQ6bNgwqd+0adOs9llUVISBAwc+VjDr27cvgoKCkJeXh5s3b0Kn06Ffv34IDAy06rt69Wr06dMH+fn5kMlkUnvz6FVbWjs2ALh27ZrF+2HDhsFgMODVV1+1CsinTp3CtWvX4OjoCJPJhNOnT6Nnz57w8vKCl5eXtL3o6GgUFBQgLi4OI0aMeGR9RNQ5nGNGRO3m7u6O4cOHo7CwsMVwdvbsWRw6dAhubm5Wj2/oqNmzZ+P27dvYvn27Rfu2bdusRooGDx4MAPjnn38s2h9+9INMJsPkyZNRUlIizbVqVlJSgtjYWGRkZDx2jWFhYWhqakJ+fj5KS0sRHBwMR0dHq343btyATCazCGUAsHnzZgBtXyJuPrZ///3Xor2srMwq2M2aNQsArEbM6uvrER8fj5iYGBiNRjQ1NUGj0WD58uUWj/yQyWR4+eWXATzeqCERdR7PNCJqt+eeew6ff/45oqOjERkZiZCQEGlkrKKiAnl5eejduze++OILaYSnsxYtWoRDhw7hq6++wpkzZ6BQKHDy5EkUFBRY9Q0NDcX333+PtWvXorq6Gs7Ozjh8+DBOnz5tNectJSUFGo0GixcvhlqtxqhRo1BZWYmsrCwMHDgQiYmJj13j5MmT4ebmhrS0NDQ2NiIiIqLFfn5+ftDr9fjggw/g7e2NhoYGFBYW4sSJE3BwcMCtW7da3YerqysmTZqEI0eO4MMPP8TUqVNRWVmJPXv2wNXVFQaDQeobHh6OwsJC/Pjjj7hw4QJ8fX1hMpnw008/oaqqCitWrJDm+y1ZsgRarRYajQZBQUHo3bs3/vzzT+j1evj7+0sBjYieLAYzIuoQT09P5OTkYMeOHSgtLUVeXh4AYMiQIYiIiMCiRYvw0ksvddn+evbsia1btyI9PR06nQ4FBQUYN24cMjMzsWDBAou+Li4u2Lx5M7799ltkZGTA0dER3t7e2L17N4KDgy36Nv8Y+6ZNm1BYWIisrCw4OzsjMDAQMTEx7Q4kYWFhSE1NhVwul+42fdgnn3wCJycnFBUV4ddff4WTkxPkcjl27tyJXbt2Yf/+/bhy5UqrvxSQlpaG9evXo7i4GAcPHsSYMWOwYcMGFBcXWwQze3t7ZGRkYNu2bdDpdEhNTUWfPn0wcuRIfPTRR9IzzAAgNjYWgwcPxp49e5Ceno7Gxka4uLggISEB77zzTrs+AyLqODshhHjaRRARdYZcLseUKVOwc+fOp10KEVGncI4ZERERkY1gMCMiIiKyEQxmRERERDaCc8yIiIiIbARHzIiIiIhsBIMZERERkY1gMCMiIiKyEQxmRERERDaCwYyIiIjIRjCYEREREdkIBjMiIiIiG8FgRkRERGQjGMyIiIiIbMT/AbGGW6EYgfoQAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 700x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fs.plot_unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, we can access a dataframe with the number of unique values per feature."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>nunique</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>OBS_60_CNT_SOCIAL_CIRCLE</th>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CNT_CHILDREN</th>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FLAG_CONT_MOBILE</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>REG_REGION_NOT_LIVE_REGION</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BASEMENTAREA_MEDI</th>\n",
       "      <td>1618</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            nunique\n",
       "OBS_60_CNT_SOCIAL_CIRCLE         21\n",
       "CNT_CHILDREN                      7\n",
       "FLAG_CONT_MOBILE                  2\n",
       "REG_REGION_NOT_LIVE_REGION        2\n",
       "BASEMENTAREA_MEDI              1618"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fs.unique_stats.sample(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3. Collinear (highly correlated) Features\n",
    "\n",
    "This method finds pairs of collinear features based on the Pearson correlation coefficient. For each pair above the specified threshold (in terms of absolute value), it identifies one of the variables to be removed. We need to pass in a `correlation_threshold`. \n",
    "\n",
    "This method is based on code found at https://chrisalbon.com/machine_learning/feature_selection/drop_highly_correlated_features/\n",
    "\n",
    "For each pair, the feature that will be removed is the one that comes last in terms of the column ordering in the dataframe. (This method does not one-hot encode the data beforehand unless `one_hot=True`. Therefore correlations are only calculated between numeric columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "24 features with a correlation magnitude greater than 0.97.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "fs.identify_collinear(correlation_threshold=0.975)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['AMT_GOODS_PRICE',\n",
       " 'FLAG_EMP_PHONE',\n",
       " 'YEARS_BUILD_MODE',\n",
       " 'COMMONAREA_MODE',\n",
       " 'ELEVATORS_MODE']"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "correlated_features = fs.ops['collinear']\n",
    "correlated_features[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can view a heatmap of the correlations above the threhold. The features which will be dropped are on the x-axis. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fs.plot_collinear()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To plot all of the correlations in the data, we can pass in `plot_all = True` to the `plot_collinear` function. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fs.plot_collinear(plot_all=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "21 features with a correlation magnitude greater than 0.98.\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fs.identify_collinear(correlation_threshold=0.98)\n",
    "fs.plot_collinear()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To view the details of the corelations above the threshold, we access the `record_collinear` attribute which is a dataframe. The `drop_feature` will be removed and for each feature that will be removed, there may be several correlations it has with the `corr_feature` that are above the `correlation_threshold`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>drop_feature</th>\n",
       "      <th>corr_feature</th>\n",
       "      <th>corr_value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AMT_GOODS_PRICE</td>\n",
       "      <td>AMT_CREDIT</td>\n",
       "      <td>0.987232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>FLAG_EMP_PHONE</td>\n",
       "      <td>DAYS_EMPLOYED</td>\n",
       "      <td>-0.999533</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>YEARS_BUILD_MODE</td>\n",
       "      <td>YEARS_BUILD_AVG</td>\n",
       "      <td>0.992120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>COMMONAREA_MODE</td>\n",
       "      <td>COMMONAREA_AVG</td>\n",
       "      <td>0.988074</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>FLOORSMAX_MODE</td>\n",
       "      <td>FLOORSMAX_AVG</td>\n",
       "      <td>0.984663</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       drop_feature     corr_feature  corr_value\n",
       "0   AMT_GOODS_PRICE       AMT_CREDIT    0.987232\n",
       "1    FLAG_EMP_PHONE    DAYS_EMPLOYED   -0.999533\n",
       "2  YEARS_BUILD_MODE  YEARS_BUILD_AVG    0.992120\n",
       "3   COMMONAREA_MODE   COMMONAREA_AVG    0.988074\n",
       "4    FLOORSMAX_MODE    FLOORSMAX_AVG    0.984663"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fs.record_collinear.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4. Zero Importance Features\n",
    "\n",
    "This method relies on a machine learning model to identify features to remove. It therefore requires a supervised learning problem with labels. The method works by finding feature importances using a gradient boosting machine implemented in the [LightGBM library](http://lightgbm.readthedocs.io/en/latest/Quick-Start.html). \n",
    "\n",
    "To reduce variance in the calculated feature importances, the model is trained a default 10 times. The model is also by default trained with early stopping using a validation set (15% of the training data) to identify the optimal number of estimators to train. The following parameters can be passed to the `identify_zero_importance` method:\n",
    "\n",
    "* `task`: either `classification` or `regression`. The metric and labels must match with the task\n",
    "* `eval_metric`: the metric used for early stopping (for example `auc` for classification or `l2` for regression). To see a list of available metrics, refer to the [LightGBM docs](http://testlightgbm.readthedocs.io/en/latest/Parameters.html#metric-parameters)\n",
    "* `n_iterations`: number of training runs. The feature importances are averaged over the training runs (default = 10)\n",
    "* `early_stopping`: whether to use early stopping when training the model (default = True). [Early stopping](https://en.wikipedia.org/wiki/Early_stopping) stops training estimators (decision trees) when the performance on a validation set no longer decreases for a specified number of estimators (100 by default in this implementation). Early stopping is a form of regularization used to prevent overfitting to training data\n",
    "\n",
    "The data is first one-hot encoded for use in the model. This means that some of the zero importance features may be created from one-hot encoding. To view the one-hot encoded columns, we can access the `one_hot_features` of the `FeatureSelector`.\n",
    "\n",
    "__Note of caution__: in contrast to the other methods, the feature imporances from a model are non-deterministic (have a little randomness). The results of running this method can change each time it is run. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Gradient Boosting Model\n",
      "\n",
      "Training until validation scores don't improve for 100 rounds.\n",
      "Early stopping, best iteration is:\n",
      "[54]\tvalid_0's auc: 0.747159\n",
      "Training until validation scores don't improve for 100 rounds.\n",
      "Early stopping, best iteration is:\n",
      "[61]\tvalid_0's auc: 0.753946\n",
      "Training until validation scores don't improve for 100 rounds.\n",
      "Early stopping, best iteration is:\n",
      "[191]\tvalid_0's auc: 0.733081\n",
      "Training until validation scores don't improve for 100 rounds.\n",
      "Early stopping, best iteration is:\n",
      "[68]\tvalid_0's auc: 0.764867\n",
      "Training until validation scores don't improve for 100 rounds.\n",
      "Early stopping, best iteration is:\n",
      "[112]\tvalid_0's auc: 0.736353\n",
      "Training until validation scores don't improve for 100 rounds.\n",
      "Early stopping, best iteration is:\n",
      "[115]\tvalid_0's auc: 0.754867\n",
      "Training until validation scores don't improve for 100 rounds.\n",
      "Early stopping, best iteration is:\n",
      "[142]\tvalid_0's auc: 0.759172\n",
      "Training until validation scores don't improve for 100 rounds.\n",
      "Early stopping, best iteration is:\n",
      "[62]\tvalid_0's auc: 0.754934\n",
      "Training until validation scores don't improve for 100 rounds.\n",
      "Early stopping, best iteration is:\n",
      "[29]\tvalid_0's auc: 0.763939\n",
      "Training until validation scores don't improve for 100 rounds.\n",
      "Early stopping, best iteration is:\n",
      "[108]\tvalid_0's auc: 0.739503\n",
      "\n",
      "66 features with zero importance after one-hot encoding.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "fs.identify_zero_importance(task = 'classification', eval_metric = 'auc', \n",
    "                            n_iterations = 10, early_stopping = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Running the gradient boosting model requires one hot encoding the features. These features are saved in the `one_hot_features` attribute of the `FeatureSelector`. The original features are saved in the `base_features`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 121 original features\n",
      "There are 134 one-hot features\n"
     ]
    }
   ],
   "source": [
    "one_hot_features = fs.one_hot_features\n",
    "base_features = fs.base_features\n",
    "print('There are %d original features' % len(base_features))\n",
    "print('There are %d one-hot features' % len(one_hot_features))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `data` attribute of the `FeatureSelector` holds the original dataframe. After one-hot encoding, the `data_all` attribute holds the original data plus the one-hot encoded features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>NAME_CONTRACT_TYPE_Cash loans</th>\n",
       "      <th>NAME_CONTRACT_TYPE_Revolving loans</th>\n",
       "      <th>CODE_GENDER_F</th>\n",
       "      <th>CODE_GENDER_M</th>\n",
       "      <th>CODE_GENDER_XNA</th>\n",
       "      <th>FLAG_OWN_CAR_N</th>\n",
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       "      <th>AMT_REQ_CREDIT_BUREAU_DAY</th>\n",
       "      <th>AMT_REQ_CREDIT_BUREAU_WEEK</th>\n",
       "      <th>AMT_REQ_CREDIT_BUREAU_MON</th>\n",
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       "<p>10 rows × 255 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   NAME_CONTRACT_TYPE_Cash loans  NAME_CONTRACT_TYPE_Revolving loans  \\\n",
       "0                              1                                   0   \n",
       "1                              0                                   1   \n",
       "2                              1                                   0   \n",
       "3                              1                                   0   \n",
       "4                              1                                   0   \n",
       "5                              1                                   0   \n",
       "6                              1                                   0   \n",
       "7                              1                                   0   \n",
       "8                              1                                   0   \n",
       "9                              1                                   0   \n",
       "\n",
       "   CODE_GENDER_F  CODE_GENDER_M  CODE_GENDER_XNA  FLAG_OWN_CAR_N  \\\n",
       "0              1              0                0               0   \n",
       "1              1              0                0               0   \n",
       "2              1              0                0               1   \n",
       "3              1              0                0               1   \n",
       "4              1              0                0               1   \n",
       "5              1              0                0               0   \n",
       "6              1              0                0               1   \n",
       "7              1              0                0               1   \n",
       "8              1              0                0               1   \n",
       "9              0              1                0               0   \n",
       "\n",
       "   FLAG_OWN_CAR_Y  FLAG_OWN_REALTY_N  FLAG_OWN_REALTY_Y  \\\n",
       "0               1                  1                  0   \n",
       "1               1                  0                  1   \n",
       "2               0                  0                  1   \n",
       "3               0                  0                  1   \n",
       "4               0                  0                  1   \n",
       "5               1                  1                  0   \n",
       "6               0                  0                  1   \n",
       "7               0                  0                  1   \n",
       "8               0                  0                  1   \n",
       "9               1                  0                  1   \n",
       "\n",
       "   NAME_TYPE_SUITE_Children             ...              FLAG_DOCUMENT_18  \\\n",
       "0                         0             ...                             0   \n",
       "1                         0             ...                             0   \n",
       "2                         0             ...                             0   \n",
       "3                         0             ...                             0   \n",
       "4                         0             ...                             0   \n",
       "5                         0             ...                             0   \n",
       "6                         0             ...                             0   \n",
       "7                         0             ...                             0   \n",
       "8                         0             ...                             0   \n",
       "9                         0             ...                             0   \n",
       "\n",
       "   FLAG_DOCUMENT_19  FLAG_DOCUMENT_20  FLAG_DOCUMENT_21  \\\n",
       "0                 0                 0                 0   \n",
       "1                 0                 0                 0   \n",
       "2                 0                 0                 0   \n",
       "3                 0                 0                 0   \n",
       "4                 0                 0                 0   \n",
       "5                 0                 0                 0   \n",
       "6                 0                 0                 0   \n",
       "7                 0                 0                 0   \n",
       "8                 0                 0                 0   \n",
       "9                 0                 0                 0   \n",
       "\n",
       "   AMT_REQ_CREDIT_BUREAU_HOUR  AMT_REQ_CREDIT_BUREAU_DAY  \\\n",
       "0                         0.0                        0.0   \n",
       "1                         0.0                        0.0   \n",
       "2                         0.0                        0.0   \n",
       "3                         0.0                        0.0   \n",
       "4                         0.0                        0.0   \n",
       "5                         NaN                        NaN   \n",
       "6                         0.0                        0.0   \n",
       "7                         0.0                        0.0   \n",
       "8                         0.0                        0.0   \n",
       "9                         NaN                        NaN   \n",
       "\n",
       "   AMT_REQ_CREDIT_BUREAU_WEEK  AMT_REQ_CREDIT_BUREAU_MON  \\\n",
       "0                         0.0                        0.0   \n",
       "1                         0.0                        0.0   \n",
       "2                         1.0                        0.0   \n",
       "3                         0.0                        0.0   \n",
       "4                         0.0                        0.0   \n",
       "5                         NaN                        NaN   \n",
       "6                         0.0                        1.0   \n",
       "7                         0.0                        0.0   \n",
       "8                         0.0                        0.0   \n",
       "9                         NaN                        NaN   \n",
       "\n",
       "   AMT_REQ_CREDIT_BUREAU_QRT  AMT_REQ_CREDIT_BUREAU_YEAR  \n",
       "0                        0.0                         1.0  \n",
       "1                        0.0                         0.0  \n",
       "2                        0.0                         7.0  \n",
       "3                        0.0                         1.0  \n",
       "4                        1.0                         1.0  \n",
       "5                        NaN                         NaN  \n",
       "6                        0.0                         0.0  \n",
       "7                        1.0                         0.0  \n",
       "8                        0.0                         0.0  \n",
       "9                        NaN                         NaN  \n",
       "\n",
       "[10 rows x 255 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fs.data_all.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are a number of methods we can use to inspect the results of the feature importances. First we can access the list of features with zero importance. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['ORGANIZATION_TYPE_Trade: type 1',\n",
       " 'ORGANIZATION_TYPE_Trade: type 5',\n",
       " 'WALLSMATERIAL_MODE_Monolithic',\n",
       " 'ORGANIZATION_TYPE_Trade: type 6',\n",
       " 'ORGANIZATION_TYPE_Transport: type 1']"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "zero_importance_features = fs.ops['zero_importance']\n",
    "zero_importance_features[10:15]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot Feature Importances\n",
    "\n",
    "The feature importance plot using `plot_feature_importances` will show us the `plot_n` most important features (on a normalized scale where the features sum to 1). It also shows us the cumulative feature importance versus the number of features. \n",
    "\n",
    "When we plot the feature importances, we can pass in a `threshold` which identifies the number of features required to reach a specified cumulative feature importance. For example, `threshold = 0.99` will tell us the number of features needed to account for 99% of the total importance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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X1KRJE3Xv3r1YcRT0HG/ls1ESoaGhKleunH7++WdNnTrVaivTt99+qzlz5ki6lqCQpDZt2qht27Yym80aOnSoduzYYYzPysrSggULFB8fL0kaOnSosbIo980qMTEx+vbbb61i+Pnnn42VG9e/FhgAbIkaHAAA4B+lbNmyWrBggUJDQ7Vr1y498cQTql+/vipUqKD9+/cbNR8iIyOt/oV/7NixGj58uP7880917NjRWPZ/4MABXblyRV5eXnJwcNDBgwfzbElo2rSp9u7dq2XLlunbb79V586dNXz4cFWpUkVDhgzRokWLtGHDBn333XeqU6eODh06pDNnzqhfv376+uuvrb40l+Y1/h1VrlxZ5cqV07PPPqv69eurfPny2rt3r7Kzs9WkSRPNnDnTamVFq1atNH36dL3yyiuKi4vTJ598ooYNG+rixYvGW1NMJpNiYmLk7OxcrFiaNm2qTZs26dNPP9WePXvUpk0bTZ48+ZY+GyXRqFEjvfnmmwoPD9f777+v9evXq0GDBsrIyDCKyj799NN65plnjDkzZ87UsGHDlJKSon79+qlOnTqqUqWK0tLSjCKtgwYNUt++fY05AwcO1JYtW/Ttt99q6NCh8vT0lKenp06fPm0keTp06JDva3wBwBZYwQEAAP5xvLy8tH79eoWHh+v+++/XiRMntHfvXlWsWFFdunTRe++9p0GDBlnNad++vRISEtSpUyd5eHgoNTVVR44ckclk0rhx45SYmKgnn3xSkvJsqZgwYYK6dOmiChUqKDU11WqbwNixYzVr1iy1bt1aV69e1b59+1S/fn3NnDlTr7322h29xr8jFxcXxcfHKzg4WJmZmdq/f78aNWqk8ePH68MPP1TNmjXzzOnevbsSExPVu3dvubu7648//tDp06fl6+urV199VQkJCfLy8ip2LEOHDlVwcLDc3Ny0f/9+7dmzR9KtfTZK6tFHH1ViYqKCg4NVtWpV7dmzR+fOnZO/v7/mzp2rqKgoq8RP1apV9f7772vq1Kny8/PTuXPntGfPHjk5Oaldu3ZasGCBJk6caHUOR0dHvfPOO5o4caJatWqly5cva/fu3bp06ZIeeughvfXWW1qwYAGvigXwt+FguVMvagcAAACKaN26dYqMjFSNGjXybI8AACA/rOAAAAAAAAB2jwQHAAAAAACweyQ4AAAAAACA3SPBAQAAAAAA7B5FRgEAAAAAgN3jnU4A7rg2bdooKytLHh4etg4FAAAAgA2dOHFCzs7O+vnnn2/5WCQ4ANxxV65cUXZ2tq3DAAAAAGBjZrNZt2tjCQkOAHecp6enJCkpKcnGkQAAAACwpY4dO962Y1FkFAAAAAAA2D0SHAAAAAAAwO6R4AAAAAAAAHaPBAcAAAAAALB7JDgAAAAAAIDdI8EBAAAAAADsHgkOAAAAAABg90hwAAAAAAAAu0eCAwAAAAAA2D0SHAAAAAAAwO6R4AAAAAAAAHaPBAcAAAAAALB7JDgAAAAAAIDdI8EBAAAAAADsHgkOAAAAAABg90hwAAAAAAAAu0eCAwAAAAAA2D0SHAAAAAAAwO452ToAAP9MZrNZO3bssHUYAAAAwD+Ku7u76tata+swSgUJDgA2kZ6ertatW9s6DAAAAOAfpXwFF+3ZveuuTHKQ4ABgEzk5Oar+xDiVre5l61AAAACAf4SrGWnK2BCtkydPkuAAgNupbHUvlavZyNZhAAAAALgLUGQUAAAAAADYPRIcAAAAAADA7pHgAAAAAAAAdo8EBwAAAAAAsHskOAAAAAAAgN0jwQEAAAAAAOwer4lFqYmIiND69esL7Hdzc9OmTZvUo0cPXbhwQZ9++qmqVq1qNWbz5s0aNmyYIiIi5OrqqsjIyELPm5SUpDp16hQ5zuTkZC1dulS//PKLLly4IHd3dwUEBOjFF1/M993QycnJWrFihXbs2KHMzEx5enoqICBAgwcPVsOGDa3GdujQQX5+fpoxY0a+5/b29tbIkSM1atQoY/yhQ4esxpQpU0aVKlVSkyZN9MILLygwMDDPcbZu3ar3339fKSkpyszMVM2aNdW+fXuFhobK3d3dGFeUZ7Jt27aCb1Y+0tLSFB0drZ9//lmXLl1S48aNFRYWpoCAgGIdBwAAAABuBQkOlCoPDw/FxMTk2+fk5CQXFxfNnDlT/fr106RJk6zGHj58WOHh4QoKClJISIhOnTqlNWvWGP3ffPONFi5cqJiYGHl4eBjtnp6eRY5v69atCg0NVceOHTVt2jS5urrq4MGDio2NVXBwsOLj462SHEuWLNHs2bMVGBioyMhIeXp66sCBA/rwww/Vo0cPRUVF6fHHHy/OLcqjXbt2Gj58uPG72WzWwYMHtWTJEg0bNkxr166Vt7e30R8dHa2lS5eqa9euevnll+Xm5qa9e/dq6dKl2rhxo1avXq3atWsb4wt7JsVx5swZ9e/fX66urpo4caIqVaqkhIQEPffcc1q5cqX8/PyKefUAAAAAUDIkOFCqnJ2d1bJly5uOadGihUaMGKG3335bCQkJ6tWrl65evaoxY8aofPnyxuqHatWqqVq1asa81NRUSdJ9991XrBUb11u0aJGaN2+uefPmGW3+/v5q166dOnfurHfffVeTJ0+WJG3atEnR0dEaPny4wsLCjPF+fn7q3r27xo0bp4iICJlMJjVu3LhE8UjXrvPGe9amTRu1bNlSjz76qBITExUeHi5J+uyzz7RkyRJFRkYqJCTEGP+vf/1LQUFB6t69u15//XUtWrTI6CvKMymq9evXKyMjQ3FxcapRo4Yk6aGHHlK3bt20fPlyEhwAAAAA7hhqcOBvYdiwYfL19dX06dOVlpamuXPn6vfff1d0dLTc3NxK7bwnT57Mt93T01OvvPKK1XaQmJgY1a9fX6NHj84zvmzZspoyZYocHR21dOnSUom1cuXKedoWL16sRo0aadCgQXn66tatq/DwcLVu3Vo5OTmlElONGjUUEhJiJDeka1tq6tatq4MHD5bKOQEAAAAgP6zgQKkzm835tjs6OsrBwcH488yZM9WtWzeNGDFCf/zxh8LCwtSmTZtSjS0oKEjLli3TgAED1K1bN/n7+8vLy0uSFBwcbIw7deqUfv/9dz333HNGzDeqWrWqHnzwQSUlJd1STBaLxeqemc1mHThwQG+99ZacnJz0xBNPSJJOnDih3bt3KzQ0tMCY+vbtm297UZ5JUTz22GN67LHHrNrOnDmj7du3U4MDAAAAwB1FggOl6tChQ/Lx8cm3LywszKrWRJ06dTRu3DhNmTJF3t7eev7550s9vrCwMJ07d05r167V9u3bJV1blRAUFKRBgwYZRUNzC38WthWmXr16SkpK0tmzZ1WlSpUSxfTxxx/r448/tmpzcnJSs2bNFBsbq6ZNm0qSjh49WqSYblScZ1Jc2dnZevnll3Xx4kUNHTq0xMcBAAAAgOIiwYFS5eHhoYULF+bbd/22BknKycnRl19+KQcHB/3555/asWNHqa/gcHZ21tSpUzVq1Cht3rxZP/74o7Zt26Y1a9Zo3bp1io6OVpcuXWSxWCRd24pyM46OjpJkjC+KG1dMtG/fXiNGjJDFYtH+/fs1e/Zs1ahRQ/Pnz7cqoFqmzLUdZsXdflKcZ1IcV69e1YQJE/TVV1/ptddeU/PmzUt8LAAAAAAoLhIcKFXOzs5F/qK7ePFibdmyRTNnztSsWbMUHh6uxMTEfGtP3G4eHh7q1auXevXqJUnatm2bXnrpJU2ZMkWdO3c23kJy4ytcb5SWliYXFxejboiLi4uysrLyHZvbXqFCBat2Nzc34561aNFCPj4+6tmzp4YOHaq4uDiVK1dOklSrVi05ODjcNKbMzEw5OjqqYsWKRltxnklRnT17ViNHjtRPP/2kV199Vf369butxwcAAACAwlBkFH8LycnJiomJUZ8+ffTUU08pKipKhw8f1muvvVZq50xJSdGDDz6oH374IU+fv7+/hgwZooyMDJ0+fVrVq1dXy5YttXHjxgJXZ5w/f14//PCDOnbsaLS5u7vr+PHj+Y7P3WLi7u5+0zgbNmyosLAw7d69W++8847RXq1aNfn4+Oi7774rMKaFCxfK399fR44cuek5bsWRI0fUt29f/frrr5o9e7aeffbZUjsXAAAAABSEBAds7uzZs3rppZdUt25dRUZGSpICAwPVv39/bdiwQYmJiaVy3nvvvVeXLl3SqlWr8t3msW/fPnl4eBivph05cqRSU1M1d+7cPGOzs7M1efJkXb58WaGhoUa7n5+ffvvtNx0+fDjPnM8//1yOjo564IEHCo11wIABMplMio2N1b59+4z2IUOGaO/evVq9enWeOampqYqPj5efn59q1apV6DlK4vz58woJCdHx48cVGxubp+AoAAAAANwpbFFBqcrKytKvv/5aYL/JZFJkZKROnDihuLg4q+0a48eP19atWzV16lS1bt262MU0C1OlShVNmDBBkydP1jPPPKPevXvLy8tL586d05dffqn169dr1qxZRo2Mtm3bKiIiQm+99ZZ27typHj16yNPTU+np6frwww+1a9cuTZ8+XU2aNDHO0b9/f8XHx6t///4KDQ1V48aNdfnyZW3ZskWrV69WaGio7rnnnkJjdXJy0sSJExUSEqJp06Zp+fLlkq69xWTLli2aPn26UlJS1LVrV1WsWFH//e9/FRsbK1dXV0VFRVkdqyjPxMXFpUj3cN68edq/f79GjRqlsmXLWh3X2dnZKIgKAAAAAKWNBAdK1YkTJ9SnT58C+0eNGqWkpCRNmDAhz5fhcuXKadasWQoODtb48eP13nvvGUU8b5e+ffuqXr16WrVqlWbPnq0zZ86oYsWKatGihVauXCl/f3+r8YMHD1arVq20cuVKvfnmmzp16pQ8PDwUGBio6dOnq1GjRlbjXV1dlZCQoAULFig2NlbHjx9X+fLl1aBBA02bNk3du3cvcqwBAQHq0qWLvvjiC3311Vfq1KmTJGnatGny9/dXXFycJk+erPPnz6t27drq2bOnQkNDjRUouQp7JgkJCUWu0bFx40ZJ0vz58zV//nyrvtq1a+vrr78u8vUBAAAAwK1wsBTndQ8AcBt07NhRqamputxujMrVbFT4BAAAAAC37MrRP3V05RglJyfL19fX1uFIklHDMCkp6ZaPxQoO3HWys7OL9JpWJyc+/gUxm82FjilTpozxqloAAAAAsDW+4eGuExISou3btxc6bs+ePXcgGvuTnp5u9SaYgvTo0UMzZsy4AxEBAAAAQOFIcOCuM2XKFF24cMHWYdgtT09PJSQkFDquatWqdyAaAAAAACgaEhy46zRo0MDWIdg1Z2fnIhcZBQAAAIC/CzbQAwAAAAAAu8cKDgA2czUjzdYhAAAAAP8Yd/vfv0lwALCJMmXKKGNDtK3DAAAAAP5Ryldwkbu7u63DKBUkOADYRJ06dRQfH2/rMAAAAIB/FHd3d9WtW9fWYZQKEhwAbMLJyUm+vr62DgMAAADAXYIiowAAAAAAwO6R4AAAAAAAAHaPBAcAAAAAALB71OAAYBNms1k7duywdRgAAABAvu7mYpx3KxIcAGwiPT1drVu3tnUYAAAAQL7KV3DRnt27SHLYERIcAGwiJydH1Z8Yp7LVvWwdCgAAAGDlakaaMjZE6+TJkyQ47AgJDgA2U7a6l8rVbGTrMAAAAADcBSgyCgAAAAAA7B4JDgAAAAAAYPdIcAAAAAAAALtHggMAAAAAANg9EhwAAAAAAMDukeAAAAAAAAB2j9fEotRERERo/fr1Bfa7ublp06ZN6tGjhy5cuKBPP/1UVatWtRqzefNmDRs2TBEREXJ1dVVkZGSh501KSlKdOnWKHGdycrKWLl2qX375RRcuXJC7u7sCAgL04osv5vvO6+TkZK1YsUI7duxQZmamPD09FRAQoMGDB6thw4ZWYzt06CA/Pz/NmDEj33N7e3tr5MiRGjVqlDH+0KFDVmPKlCmjSpUqqUmTJnrhhRcUGBiY5zhbt27V+++/r5SUFGVmZqpmzZpq3769QkND5e7ubowryjPZtm1bwTcrH7t379asWbP066+/ytHRUUFBQRo3bpw8PT2LdRwAAAAAuBUkOFCqPDw8FBMTk2+fk5NipafYAAAgAElEQVSTXFxcNHPmTPXr10+TJk2yGnv48GGFh4crKChIISEhOnXqlNasWWP0f/PNN1q4cKFiYmLk4eFhtBfni/XWrVsVGhqqjh07atq0aXJ1ddXBgwcVGxur4OBgxcfHWyU5lixZotmzZyswMFCRkZHy9PTUgQMH9OGHH6pHjx6KiorS448/XpxblEe7du00fPhw43ez2ayDBw9qyZIlGjZsmNauXStvb2+jPzo6WkuXLlXXrl318ssvy83NTXv37tXSpUu1ceNGrV69WrVr1zbGF/ZMiuPo0aMaNGiQ6tevr1mzZunSpUuaM2eOBg8erMTExGIfDwAAAABKim8fKFXOzs5q2bLlTce0aNFCI0aM0Ntvv62EhAT16tVLV69e1ZgxY1S+fHlj9UO1atVUrVo1Y15qaqok6b777ivWio3rLVq0SM2bN9e8efOMNn9/f7Vr106dO3fWu+++q8mTJ0uSNm3apOjoaA0fPlxhYWHGeD8/P3Xv3l3jxo1TRESETCaTGjduXKJ4pGvXeeM9a9OmjVq2bKlHH31UiYmJCg8PlyR99tlnWrJkiSIjIxUSEmKM/9e//qWgoCB1795dr7/+uhYtWmT0FeWZFNVHH32kS5cuadGiRXJzczPiHzhwoLZu3aq2bdvelvMAAAAAQGGowYG/hWHDhsnX11fTp09XWlqa5s6dq99//13R0dHGF+fScPLkyXzbPT099corr1htB4mJiVH9+vU1evToPOPLli2rKVOmyNHRUUuXLi2VWCtXrpynbfHixWrUqJEGDRqUp69u3boKDw9X69atlZOTUyoxDRw4UO+//77VMypbtqwkKSsrq1TOCQAAAAD5YQUHSp3ZbM633dHRUQ4ODsafZ86cqW7dumnEiBH6448/FBYWpjZt2pRqbEFBQVq2bJkGDBigbt26yd/fX15eXpKk4OBgY9ypU6f0+++/67nnnjNivlHVqlX14IMPKikp6ZZislgsVvfMbDbrwIEDeuutt+Tk5KQnnnhCknTixAnt3r1boaGhBcbUt2/ffNuL8kyK4vpVNVeuXNHOnTs1depU3XvvvXrooYeKfBwAAAAAuFUkOFCqDh06JB8fn3z7wsLCrGpN1KlTR+PGjdOUKVPk7e2t559/vtTjCwsL07lz57R27Vpt375dklSjRg0FBQVp0KBBRtHQ3MKfhW2FqVevnpKSknT27FlVqVKlRDF9/PHH+vjjj63anJyc1KxZM8XGxqpp06aSrtW/KEpMNyrOMymOJ598UgcOHFC5cuU0b948lStXrkTHAQAAAICSIMGBUuXh4aGFCxfm21ejRg2r33NycvTll1/KwcFBf/75p3bs2FHqKzicnZ01depUjRo1Sps3b9aPP/6obdu2ac2aNVq3bp2io6PVpUsXWSwWSf+3/aIgjo6OkmSML4obV0y0b99eI0aMkMVi0f79+zV79mzVqFFD8+fPtyqgWqbMtR1mxd1+UpxnUhy5tUrWrVunF198UTNmzFC3bt1KfDwAAAAAKA4SHChVzs7Oat68eZHGLl68WFu2bNHMmTM1a9YshYeHKzExMd/aE7ebh4eHevXqpV69ekmStm3bppdeeklTpkxR586djbeQ3PgK1xulpaXJxcXFqEnh4uJSYC2K3PYKFSpYtbu5uRn3rEWLFvLx8VHPnj01dOhQxcXFGSsjatWqJQcHh5vGlJmZKUdHR1WsWNFoK84zKY7ceiWBgYE6duyY3nnnHRIcAAAAAO4YiozibyE5OVkxMTHq06ePnnrqKUVFRenw4cN67bXXSu2cKSkpevDBB/XDDz/k6fP399eQIUOUkZGh06dPq3r16mrZsqU2btxY4OqM8+fP64cfflDHjh2NNnd3dx0/fjzf8blbTNzd3W8aZ8OGDRUWFqbdu3frnXfeMdqrVasmHx8ffffddwXGtHDhQvn7++vIkSM3PUdJbd26VZs3b87T3qxZs1I7JwAAAADkhwQHbO7s2bN66aWXVLduXUVGRkq6tgqgf//+2rBhgxITE0vlvPfee68uXbqkVatW5bvNY9++ffLw8DCKaI4cOVKpqamaO3dunrHZ2dmaPHmyLl++rNDQUKPdz89Pv/32mw4fPpxnzueffy5HR0c98MADhcY6YMAAmUwmxcbGat++fUb7kCFDtHfvXq1evTrPnNTUVMXHx8vPz0+1atUq9BwlsX79eoWHh+v8+fNGm9ls1tatW9WkSZNSOScAAAAA5IctKihVWVlZ+vXXXwvsN5lMioyM1IkTJxQXF2e1XWP8+PHaunWrpk6dqtatWxe7mGZhqlSpogkTJmjy5Ml65pln1Lt3b3l5eencuXP68ssvtX79es2aNcuokdG2bVtFRETorbfe0s6dO9WjRw95enoqPT1dH374oXbt2qXp06dbfbHv37+/4uPj1b9/f4WGhqpx48a6fPmytmzZotWrVys0NFT33HNPobE6OTlp4sSJCgkJ0bRp07R8+XJJ0mOPPaYtW7Zo+vTpSklJUdeuXVWxYkX997//VWxsrFxdXRUVFWV1rKI8ExcXlyLdw9DQUG3cuFHPP/+8QkNDZbFYtHr1av3111+KjY0t0jEAAAAA4HYgwYFSdeLECfXp06fA/lGjRikpKUkTJkww3g6Sq1y5cpo1a5aCg4M1fvx4vffee0YRz9ulb9++qlevnlatWqXZs2frzJkzqlixolq0aKGVK1fK39/favzgwYPVqlUrrVy5Um+++aZOnTolDw8PBQYGavr06WrUqJHVeFdXVyUkJGjBggWKjY3V8ePHVb58eTVo0EDTpk1T9+7dixxrQECAunTpoi+++EJfffWVOnXqJEmaNm2a/P39FRcXp8mTJ+v8+fOqXbu2evbsqdDQUGMFSq7CnklCQkKRa3SYTCa9//77mj17tiIjI5WVlaVWrVrpvffeU8uWLYt8bQAAAABwqxwsxXndAwDcBh07dlRqaqoutxujcjUbFT4BAAAAuIOuHP1TR1eOUXJysnx9fW0dzl0tt4ZhUlLSLR+LFRy462RnZxfpNa1OTnz8C2I2mwsdU6ZMGeNVtQAAAABga3zDw10nJCRE27dvL3Tcnj177kA09ic9Pd3qTTAF6dGjh2bMmHEHIgIAAACAwpHgwF1nypQpunDhgq3DsFuenp5KSEgodFzVqlXvQDQAAAAAUDQkOHDXadCgga1DsGvOzs5FLjIKAAAAAH8XbKAHAAAAAAB2jxUcAGzmakaarUMAAAAA8uDvqfaJBAcAmyhTpowyNkTbOgwAAAAgX+UruMjd3d3WYaAYSHAAsIk6deooPj7e1mEAAAAA+XJ3d1fdunVtHQaKgQQHAJtwcnKSr6+vrcMAAAAAcJegyCgAAAAAALB7JDgAAAAAAIDdI8EBAAAAAADsHjU4ANiE2WzWjh07bB0GAAC3HYUJAcA2SHAAsIn09HS1bt3a1mEAAHDbla/goj27d5HkAIA7jAQHAJvIyclR9SfGqWx1L1uHAgDAbXM1I00ZG6J18uRJEhwAcIeR4ABgM2Wre6lczUa2DgMAAADAXYAiowAAAAAAwO6R4AAAAAAAAHaPBAcAAAAAALB7JDgAAAAAAIDdI8EBAAAAAADsHgkOAAAAAABg93hNLEpNRESE1q9fX2C/m5ubNm3apB49eujChQv69NNPVbVqVasxmzdv1rBhwxQRESFXV1dFRkYWet6kpCTVqVOnyHEmJydr6dKl+uWXX3ThwgW5u7srICBAL774Yr7vr09OTtaKFSu0Y8cOZWZmytPTUwEBARo8eLAaNmxoNbZDhw7y8/PTjBkz8j23t7e3Ro4cqVGjRhnjDx06ZDWmTJkyqlSpkpo0aaIXXnhBgYGBeY6zdetWvf/++0pJSVFmZqZq1qyp9u3bKzQ0VO7u7sa4ojyTbdu2FXyzChEVFaWdO3dq9erVJT4GAAAAAJQECQ6UKg8PD8XExOTb5+TkJBcXF82cOVP9+vXTpEmTrMYePnxY4eHhCgoKUkhIiE6dOqU1a9YY/d98840WLlyomJgYeXh4GO2enp5Fjm/r1q0KDQ1Vx44dNW3aNLm6uurgwYOKjY1VcHCw4uPjrZIcS5Ys0ezZsxUYGKjIyEh5enrqwIED+vDDD9WjRw9FRUXp8ccfL84tyqNdu3YaPny48bvZbNbBgwe1ZMkSDRs2TGvXrpW3t7fRHx0draVLl6pr1656+eWX5ebmpr1792rp0qXauHGjVq9erdq1axvjC3smJbVkyRKtWLFCfn5+JT4GAAAAAJQUCQ6UKmdnZ7Vs2fKmY1q0aKERI0bo7bffVkJCgnr16qWrV69qzJgxKl++vLH6oVq1aqpWrZoxLzU1VZJ03333FWvFxvUWLVqk5s2ba968eUabv7+/2rVrp86dO+vdd9/V5MmTJUmbNm1SdHS0hg8frrCwMGO8n5+funfvrnHjxikiIkImk0mNGzcuUTzSteu88Z61adNGLVu21KOPPqrExESFh4dLkj777DMtWbJEkZGRCgkJMcb/61//UlBQkLp3767XX39dixYtMvqK8kyKIy0tTTNmzNCmTZtUuXLl23ZcAAAAACgOanDgb2HYsGHy9fXV9OnTlZaWprlz5+r3339XdHS03NzcSu28J0+ezLfd09NTr7zyitV2kJiYGNWvX1+jR4/OM75s2bKaMmWKHB0dtXTp0lKJNb/kweLFi9WoUSMNGjQoT1/dunUVHh6u1q1bKycnp1Rikq5tSzl48KBWrlyp++67r9TOAwAAAAA3wwoOlDqz2Zxvu6OjoxwcHIw/z5w5U926ddOIESP0xx9/KCwsTG3atCnV2IKCgrRs2TINGDBA3bp1k7+/v7y8vCRJwcHBxrhTp07p999/13PPPWfEfKOqVavqwQcfVFJS0i3FZLFYrO6Z2WzWgQMH9NZbb8nJyUlPPPGEJOnEiRPavXu3QkNDC4ypb9+++bYX5ZkU1ZgxY9S4ceNizwMAAACA24kEB0rVoUOH5OPjk29fWFiYVa2JOnXqaNy4cZoyZYq8vb31/PPPl3p8YWFhOnfunNauXavt27dLkmrUqKGgoCANGjTIKBqaW/izsK0w9erVU1JSks6ePasqVaqUKKaPP/5YH3/8sVWbk5OTmjVrptjYWDVt2lSSdPTo0SLFdKPiPJOiMJlMxRoPAAAAAKWBBAdKlYeHhxYuXJhvX40aNax+z8nJ0ZdffikHBwf9+eef2rFjR6mv4HB2dtbUqVM1atQobd68WT/++KO2bdumNWvWaN26dYqOjlaXLl1ksVgkXduKcjOOjo6SZIwvihtXPrRv314jRoyQxWLR/v37NXv2bNWoUUPz58+3KqBapsy1HWbF3X5SnGcCAAAAAPaCBAdKlbOzs5o3b16ksYsXL9aWLVs0c+ZMzZo1S+Hh4UpMTLwjhSs9PDzUq1cv9erVS5K0bds2vfTSS5oyZYo6d+5svIXkxle43igtLU0uLi5G3RAXFxdlZWXlOza3vUKFClbtbm5uxj1r0aKFfHx81LNnTw0dOlRxcXEqV66cJKlWrVpycHC4aUyZmZlydHRUxYoVjbbiPBMAAAAAsBcUGcXfQnJysmJiYtSnTx899dRTioqK0uHDh/Xaa6+V2jlTUlL04IMP6ocffsjT5+/vryFDhigjI0OnT59W9erV1bJlS23cuLHA1Rnnz5/XDz/8oI4dOxpt7u7uOn78eL7jc7eYuLu73zTOhg0bKiwsTLt379Y777xjtFerVk0+Pj767rvvCoxp4cKF8vf315EjR256DgAAAACwdyQ4YHNnz57VSy+9pLp16yoyMlKSFBgYqP79+2vDhg1KTEwslfPee++9unTpklatWpXvNo99+/bJw8PDeDXtyJEjlZqaqrlz5+YZm52drcmTJ+vy5csKDQ012v38/PTbb7/p8OHDeeZ8/vnncnR01AMPPFBorAMGDJDJZFJsbKz27dtntA8ZMkR79+7V6tWr88xJTU1VfHy8/Pz8VKtWrULPAQAAAAD2jC0qKFVZWVn69ddfC+w3mUyKjIzUiRMnFBcXZ7VdY/z48dq6daumTp2q1q1bF7uYZmGqVKmiCRMmaPLkyXrmmWfUu3dveXl56dy5c/ryyy+1fv16zZo1y6iR0bZtW0VEROitt97Szp071aNHD3l6eio9PV0ffvihdu3apenTp6tJkybGOfr376/4+Hj1799foaGhaty4sS5fvqwtW7Zo9erVCg0N1T333FNorE5OTpo4caJCQkI0bdo0LV++XJL02GOPacuWLZo+fbpSUlLUtWtXVaxYUf/9738VGxsrV1dXRUVFWR2rKM/ExcWlJLcUAAAAAGyGBAdK1YkTJ9SnT58C+0eNGqWkpCRNmDDBeDtIrnLlymnWrFkKDg7W+PHj9d577xlFPG+Xvn37ql69elq1apVmz56tM2fOqGLFimrRooVWrlwpf39/q/GDBw9Wq1attHLlSr355ps6deqUPDw8FBgYqOnTp6tRo0ZW411dXZWQkKAFCxYoNjZWx48fV/ny5dWgQQNNmzZN3bt3L3KsAQEB6tKli7744gt99dVX6tSpkyRp2rRp8vf3V1xcnCZPnqzz58+rdu3a6tmzp0JDQ40VKLkKeyYJCQnU6AAAAABgdxwsxXndAwDcBh07dlRqaqoutxujcjUbFT4BAAA7ceXonzq6coySk5Pl6+tr63AA4G8vt4ZhUlLSLR+LFRy462RnZxfpNa1OTnz8C2I2mwsdU6ZMGeNVtQAAAABga3zDw10nJCRE27dvL3Tcnj177kA09ic9Pd3qTTAF6dGjh2bMmHEHIgIAAACAwpHgwF1nypQpunDhgq3DsFuenp5KSEgodFzVqlXvQDQAAAAAUDQkOHDXadCgga1DsGvOzs4UGQUAAABgd9hADwAAAAAA7B4rOADYzNWMNFuHAADAbcX/2wDAdkhwALCJMmXKKGNDtK3DAADgtitfwUXu7u62DgMA/nFIcACwiTp16ig+Pt7WYQAAcNu5u7urbt26tg4DAP5xSHAAsAknJyf5+vraOgwAAAAAdwmKjAIAAAAAALtHggMAAAAAANg9EhwAAAAAAMDuUYMDgE2YzWbt2LHD1mEAAG5AgUwAgL0iwQHAJtLT09W6dWtbhwEAuEH5Ci7as3sXSQ4AgN0hwQHAJnJyclT9iXEqW93L1qEAAP6/qxlpytgQrZMnT5LgAADYHRIcAGymbHUvlavZyNZhAAAAALgLUGQUAAAAAADYPRIcAAAAAADA7pHgAAAAAAAAdo8EBwAAAAAAsHskOAAAAAAAgN0jwQEAAAAAAOweCQ7cFQYMGCBvb2/jp0mTJmrVqpWefvpprV69WtnZ2fnOCw8Pl7e3t5YsWZKnb9asWfL29tbatWvznbtr1y75+Pjo3//+t9GWnp6uiRMnql27dmrWrJkCAgL0wgsvaPv27cW+pgMHDlhdk7e3t3x8fNSuXTtNnjxZZ86csRrfr18/hYSEGL/PmTMnz3xvb2+1bNlSjz32mGJiYoz7Eh8fn+/YG3+OHj1qxJWYmJhv3Fu2bJG3t7d+/vnnYl8zAAAAAJSUk60DAG6Xpk2bavLkyZKk7OxsnT17Vps3b9Ybb7yh5ORkzZkzRw4ODsb48+fPa+PGjTKZTIqLi9PQoUOt+kePHq2vv/5ab775ptq1ayd3d3ejLzs7Wy+//LKqVatmnPPYsWPq06ePatasqX//+9+qVauWTp06pbi4OA0aNEjz589Xp06din1do0aN0kMPPSRJunLliv766y/FxMQoNTVVq1evvulcR0dHffDBB1Ztp06d0qeffqr58+fLbDZrzJgx6tixoxo3bmyM+frrr7V48WItXLhQ1apVM9qrVaumI0eOFPsaAAAAAKC0keDAXaNSpUpq2bKlVVuHDh1Uv359RUVFqUOHDnrqqaeMvv/85z/Kzs7WK6+8ooEDB+r7779X27ZtjX5nZ2dFRUWpX79+ev311/X2228bfcuXL9f//vc/LV26VFWqVJEkrVmzRhcuXNDKlStVqVIlY2znzp3Vs2dPzZs3r0QJDi8vL6vr8vf3l5OTkyZNmqTU1FQ1aNDgpvNvvCe59+XgwYNKSEjQmDFjVK1aNatExh9//CHpWtKoZs2axY4ZAAAAAO40tqjgrjdgwAB5enrqo48+smpfu3at/P395e/vr/r16+fpl6T7779fISEh+vzzz7Vp0yZJ17aOxMTEqF+/fnr44YeNsSdPnpSDg4MsFovVMRwdHTVu3Dj17t37tl1TblLl+hUnxVW5cuXbFQ4AAAAA2BwJDtz1HB0dFRAQoN9++01ms1mS9NdffyklJUU9evSQJD399NPatGmTjh07lmd+WFiYGjRooGnTpunKlSuaOnWqatasqfDwcKtxQUFBunjxooKDgxUbG6tdu3YpJydHktS2bVv179+/RPFbLBaZzWaZzWZdvnxZu3bt0qJFixQYGKj69esXOj93rtlsVlZWlo4dO6Zly5bpxx9/VPfu3UsU041xXf+Te80AAAAAcCexRQX/CO7u7rp69arOnDkjd3d3JSQkyNXV1dgy0r17d82dO1fx8fEaOXKk1dxy5coZW1VCQ0OVnJys999/Xy4uLlbjOnTooFdeeUVz5szRm2++KenaKomAgAD17dtXgYGBJYp9woQJmjBhglVb1apVNWvWrELnZmdny8fHJ0/7PffcozFjxig0NLREMRUUFwAAAADYCgkO/KM4ODjIbDbrk08+UadOnXTlyhVduXJF5cuXl7+/v+Lj4/Xiiy/K0dHRal7Lli0VEhKi2NhYDRs2TK1atcr3+AMGDFDPnj31/fffa+vWrdq+fbs2btyojRs3KjQ0VOPHjy92zKNHjza2wpjNZh05ckQrVqxQnz599N5776lJkyYFznV0dNSaNWskXSuqunLlSiUnJ2vSpEnq0KFDsWMpKK7r/fbbb5o6deotHRsAAAAAiosEB/4Rjh07pvLly8vNzU2bNm3SyZMntW7dOq1bty7P2E2bNuVbDLRt27aKjY3N90v99VxcXPTII4/okUcekSTt27dPEydO1LJly/T000+rYcOGxYq9Tp06at68ufF7q1at9PDDD6tdu3aKiYlRTEzMTedfP9fPz0+DBw/W6NGjtWrVKvn6+hYrlpvFlevcuXMlPiYAAAAAlBQ1OHDXy87O1vbt2+Xr6ytHR0clJCSodu3aWrVqVZ4fNze3fIuNFubq1asKCgrSO++8k6evfv36mjhxoqRrtT9uh0qVKsnLy0sHDhwo1jxHR0e98cYbcnJyUmRkpLKysm5LPAAAAABgayQ4cNf76KOPdPz4cfXr108nT57Ud999p8cff9x4g8r1P4899ph++OEHpaWlFescZcuWlYeHhxISEnT27Nk8/fv27ZODg4MaN258W64pMzNTqampqlevXrHn1qlTR8OGDdP+/fu1bNmy2xIPAAAAANgaW1Rw1zh//rx+/fVXSVJOTo5Onz6t77//XmvWrNFTTz2lRx55REuXLpXZbNbjjz+e7zF69OihDz74QHFxcRo3blyxzj9p0iQNGjRIPXr00MCBA9WkSRPl5ORo+/btWrlypZ599tkivfXkRmlpacZ1SddeR7tkyRJlZ2frueeeK/bxJGnIkCFau3atlixZou7du+uee+4p0XEAAAAA4O+CBAfuGjt37lSfPn0kSWXKlFH16tVVv359zZgxQ08++aQkaf369WrcuHGBhTlbtGihBg0aaO3atRo1apScnZ2LfP4WLVpo3bp1WrJkiVavXq0TJ07I0dFRjRs31ssvv6yePXuW6Lrmz5+v+fPnG9fl6uqqZs2aKTY2tsQ1NJydnRUREaERI0ZoxowZmjdvXomOAwAAAAB/Fw4Wi8Vi6yAA/LN07NhRqamputxujMrVbGTrcAAA/9+Vo3/q6MoxSk5OvqVC1AAAFFXHjh0lSUlJSbd8LFZwAHeY2WwudEyZMmVUpgwlcgAAAACgqEhwAHeQ2WyWj49PoeMCAgK0YsWK0g8IAAAAAO4SJDiAO8jJyUkJCQmFjqtUqdIdiAYAAAAA7h4kOIA7rHnz5rYOAQAAAADuOmzyBwAAAAAAdo8VHABs5mpGmq1DAABch/8uAwDsGQkOADZRpkwZZWyItnUYAIAblK/gInd3d1uHAQBAsZHgAGATderUUXx8vK3DAADcwN3dXXXr1rV1GAAAFBsJDgA24eTkJF9fX1uHAQAAAOAuQZFRAAAAAABg90hwAAAAAAAAu0eCAwAAAAAA2D1qcACwCbPZrB07dtg6DAD426PoJwAARUOCA4BNpKenq3Xr1rYOAwD+9spXcNGe3btIcgAAUAgSHABsIicnR9WfGKey1b1sHQoA/G1dzUhTxoZonTx5kgQHAACFIMEBwGbKVvdSuZqNbB0GAAAAgLsARUYBAAAAAIDdI8EBAAAAAADsHgkOAAAAAABg90hwAAAAAAAAu0eCAwAAAAAA2D0SHAAAAAAAwO6R4MBdY+/evRo7dqwCAwPVrFkzPfTQQxozZox27txpjImIiFCHDh3yzDWbzRo9erS8vb21YsWKYp23Q4cOivh/7N15WJT1+j/w9ww74oaImIiB4nCEQUGPhMoiKO7KohEqS6WpKbhAWoDacQEpTRO340IiauyGqQexRAQlUcsspa+5pKgB4k4aMgO/P7yYH+MMOzhi79d1zXUdns/9fD73Z2aO1zV3z3M/H38st4ZIJJK9LCws0K9fP4wdOxYbN25EWVlZo/dYVlaGnTt3wsvLCwMGDMC///1veHt7Y9++faioqJDF3bx5EyKRCKmpqUrnOXXqFEQiEU6dOiUX/+Kr6n0MCgrC9evXFeavbzwREREREVFLU1d1AkTN4ffff4e3tzesra0RFhYGAwMDFBYWYvfu3fD29kZcXBz69eun9FyJRILg4GBkZGRg6dKlmDx5crv7cIIAACAASURBVJPz6dy5MzZs2AAAqKiowOPHj3H69Gls3rwZJ06cwFdffQUtLa0GzVlSUoJp06bhzz//hK+vL6ytrVFRUYFjx44hNDQUeXl5iIiIgEAgaHTes2bNgrOzs+zvp0+f4sKFC9iyZQt+/fVXpKenQ1NTs9HxRERERERELYUFDnotfPXVV+jQoQO2b98ODQ0N2fFhw4Zh1KhR2LRpE7Zu3apwXvXixsqVK+Hl5dUs+WhqaioUVJycnNC3b1/MmTMHMTExmDVrVoPmXLRoEQoLC5GQkIA333xTdtzZ2RnGxsb4/PPPMXToULi5uTU6bxMTE4W87e3toaenh6VLlyI3NxdOTk6NjiciIiIiImopLHDQa6GkpAQAUFlZKXdcV1cXn3zyCZ4+fapwjkQiQUhICI4cOYKoqCiMHz++xfMcPnw4rK2tER8f36ACR35+PnJychASEiJX3Kji5+eH27dvo02bNs2Y7f/Xtm3bFo0nIiIiIiJqKhY46LXg7OyMrKwsvPPOO/Dy8sJbb70FMzMzCAQCjBw5UiFeKpUiJCQE6enpWLdundKYljJkyBBs2rQJt27dQrdu3ep1TnZ2NgAo7R8CPL9iZMmSJU3OraKiAhKJRPb333//jV9++QVr165Ft27dMGDAgCbFExERERERtRQWOOi1MHnyZNy5cwc7duzAsmXLAAAdO3bEkCFD4Ovri759+8piqxc3Kisrcf/+/Zeaq4GBAYDnV53Ut8BRWFgIADA2Nm6xvAAgLCwMYWFhcsd0dXUxePBgLFq0SOEKkYbGExERERERtRQ+RYVeG3PnzkV2djbWrFmDiRMnQk9PD99++y28vb0RGxsriyssLMTRo0fx3//+FwMGDMCqVatw6dKll55vQ5qBCoXP/68qlUqbde4X4+bMmYPk5GQkJiYiJCQEmpqaGD16NNatW4fu3bsrnN/QeCIiIiIiopbCKzjotdK+fXuMHTsWY8eOBQBcvHgRCxcuxOrVq2U9NjQ0NLBlyxbY29ujZ8+emDBhAubPn4+UlBRoa2u3eI7FxcUAgC5dutT7nKorPW7fvo1evXopjSkqKkLnzp0hFAqho6MDAHj27JnS2KrjVXHV1xGLxQCAvn37onPnzli0aBHU1NRkV8Y0JZ6IiIiIiKil8AoOavWKioowZMgQJCUlKYz16dMH8+bNw7Nnz1BQUAAAMDQ0hL29PYDnt3yEh4fj8uXLWLly5UvJ9+TJk+jRo0eDChxDhgwBAGRlZSkdl0ql8PT0xPTp0wE8L/RoamrKiikvqrrlpXPnzrWu6+7ujqFDhyIhIUHWB6Q544mIiIiIiJoLCxzU6hkYGEBdXR179+5FWVmZwvjVq1ehpaWFHj16KD3fw8MDI0aMQGJiIg4dOtSiuR47dgznz5+Hj49Pg84zNzeHo6Mjtm7dKivUVLd9+3aUlJTA3d0dAKCmpob+/fvjyJEjck1Aqxw+fBhvvvkmjIyM6lw7PDwcWlpaWLFiRY1XhDQlnoiIiIiIqDnwFhVq9dTU1PDpp59i9uzZ8PLywpQpU9CzZ088ffoUJ06cwJ49ezB37ly0b9++xjn+85//4Mcff8SSJUsgFoub3D/i2bNnOHfuHIDnj6599OgRzpw5g127dsHOzg5Tp05t8Jz/+c9/4O/vj0mTJsHPzw/9+vXDX3/9hcOHD+PAgQOYNGkSxo0bJ4ufO3cu/Pz84OfnhylTpqBLly64f/8+vv32W/zwww/YsGFDvdY1NjbG+++/j02bNmHnzp344IMPmjWeiIiIiIioObDAQa8FZ2dnJCYmYseOHdiyZQvu3bsHTU1N9OnTB2vXroWbm1ut53fs2BGRkZGYPn06goODsWfPHmhoaDQ6nzt37sDb2xvA80aeHTt2RPfu3bFw4UJMmjSpUXO/8cYbSEhIQGxsLA4ePIht27ZBQ0MDZmZm+PzzzzFmzBi5eBsbG8THx2Pr1q2IjIzEgwcP0L59e1hbW2PXrl2wtbWt99ozZszAN998g82bN2PChAkNjm/I7ThERERERESNIaisrKxUdRJE9M/i6uqKq1ev4m+nedAyUt40lYiIgLLCyyiMnYezZ882qDBNRETUWri6ugIAvv/++ybPxSs4iJRQ1rfiRQKBAGpqai2+jlAolD0mloiIiIiIiJRjgYNICUtLyzpjBg4ciLi4uEavcfPmTVm1sjYeHh5YtWpVo9chIiIiIiL6J2CBg0iJ5OTkOmPatGnTpDUMDQ3rtU7Hjh2btA4REREREdE/AQscREqIxeIWX0NTU/OlrENERERERPRPwBv7iYiIiIiIiKjV4xUcRKQy5XcLVJ0CEdErjf9OEhER1R8LHESkEkKhEHcPrFF1GkRErzxtHV0YGBioOg0iIqJXHgscRKQSxsbGSEpKUnUaRESvPAMDA5iYmKg6DSIiolceCxxEpBLq6uqwtbVVdRpERERERPSaYJNRIiIiIiIiImr1WOAgIiIiIiIiolaPBQ4iIiIiIiIiavXYg4OIVEIikeDHH39UdRpERK8cNhUlIiJqHBY4iEglbt68if79+6s6DSKiV462ji7+77d8FjmIiIgaiAUOIlKJiooKdBobDI1O3VWdChHRK6P8bgHuHliDkpISFjiIiIgaiAUOIlIZjU7doWXUS9VpEBERERHRa4BNRomIiIiIiIio1WOBg4iIiIiIiIhaPRY4iIiIiIiIiKjVY4GDiIiIiIiIiFo9FjiIiIiIiIiIqNVjgYOIiIiIiIiIWj0+JvYV5+vri7y8PNnfAoEAOjo6MDU1hYeHByZPngw1NTWF8xYuXIi0tDQEBwfjgw8+kBtbvXo1tm3bhoiICHh5eSmcm5+fj4kTJ2LEiBH44osvAAA3b97Epk2bcOLECdy9exdt27ZF37598d5772HgwIEN2tP169fh5uamcFxDQwMdOnRA//79ERISgu7duwMAJBIJLC0ta51z+vTpCAkJkTuWk5ODxMREnD9/HiUlJbKcp0yZAgcHB7lYR0dHODg4YOXKlbJj9dlzUlISwsPD69xzVlYWrl69infffVdhTFNTE506dcKgQYMQEhICfX19hZhr165h5MiR6NixI44fPw5NTU3ZWFlZGaytrevMoeq7MH/+fOTn5yM9PV1uvKSkBNu3b0dmZiZu376Ntm3bQiQS4Z133sGIESPkYqu+Q59++il8fHwU1qppDSIiIiIiopbCAkcr0KdPHyxduhQAIJVK8fDhQ2RlZSEiIgJnz57F2rVrIRAIZPGlpaXIyMhA7969kZiYiOnTp8uNBwUF4ejRo4iKioKTkxMMDAxkY1KpFGFhYdDX15etWVRUBG9vbxgZGWHBggXo2rUr7t27h8TERPj7+yM6OhrDhg1r8L4CAwMxZMgQ2d9Pnz7Fr7/+ii1btuDChQs4dOiQ3A95b29veHp6Kp2rS5cucn+vWLECcXFxGDFiBBYuXAhDQ0MUFxcjLS0N06ZNw8cff6y02FClvnt2dXWFubm57LyjR4/iv//9LzZv3ixXqNDX18fVq1cBAMuWLYNIJJKNlZaW4syZM9i+fTv++OMP7N27VyGflJQU9OzZE9evX0d6ejrGjx8vG9PU1ERCQoLs7/LyckydOhWTJ0/GhAkTZMe7du1a435/+eUXfPDBB9DR0YGfnx/+9a9/4fHjx8jIyMDcuXMxduxYREVFKRTTPvvsMzg6OqJbt241zk1ERERERPQysMDRCujp6aFfv35yx1xcXGBqaorIyEi4uLjI/eA9ePAgpFIpwsPD4efnh5ycHLkrFjQ1NREZGQkfHx8sX74cX375pWxsx44duHDhArZt24b27dsDABISEvDXX38hNjYWenp6stjhw4fDy8sL69evb1SBo3v37gr7sre3h66uLpYtW4a8vDy5AoiRkZFCvDJJSUmIi4vDJ598goCAALmx0aNHY+nSpVizZg1GjRoFIyMjpXPUd8/6+vpyhYzff/8dwPOiVE1z9+zZU2EfQ4YMQVlZGWJiYnDt2jWYmprKxqRSKdLS0uDt7Y2ffvoJ8fHxcp+3QCCQm6+srAzA84JGfd6v0tJSzJ49G2+88QZ27tyJtm3bysaGDRsGBwcHhISEoGfPnpg1a5ZsrKrYERYWhp07d9a5DhERERERUUtiD45WzNfXF4aGhoiPj5c7npKSAjs7O9jZ2cHU1FRhHAD69u2LgIAApKenIzMzE8DzW0c2bNgAHx8fODo6ymJLSkogEAhQWVkpN4eamhqCg4Px9ttvN+u+qhcUGqqyshKbN2+GjY0N/P39lcZUXTny4MGDGud52XsGIFdYqO748eMoLi6Gs7Mzxo0bh7Nnz8oKKc0hMTERRUVFWLZsmdIcxo0bB1dXV2zfvl1WPAGevxcfffQRcnNzlX7HiIiIiIiIXiYWOFoxNTU12Nvb4/z585BIJACAK1eu4Oeff4aHhwcAwNPTE5mZmSgqKlI4f+7cuTAzM8OKFStQVlaGZcuWwcjICAsXLpSLc3Z2xpMnTzBp0iTExMQgPz8fFRUVAAAHBwdMnTq1UflXVlZCIpHIXqWlpcjNzcWXX36J7t27o3///nLxFRUVcvHVX1UuXryIW7duYcyYMXK35VRnYGCALVu2wMLCosbcWmrPyvbx8OFDfPfdd9i5cydsbGzw5ptvysWnpKTA3NwcVlZWGDFiBPT09Jq1oJCdnQ0jI6Na+5yMGTMGpaWl+OGHH+SO+/j44K233sJnn32GW7duNVtOREREREREDcUCRytnYGCA8vJy2dUIycnJaNeuneyWEXd3dwDPb9t4kZaWFiIjI3H79m1MmzYNubm5iIqKgq6urlyci4sLwsPDUVxcjKioKLi7u2PgwIEIDAzEiRMnGp37okWLYGlpKXv1798fH374IaysrBAbGwsdHR25+OjoaLn46q87d+4AAG7cuAEACkWCF4spEolEVrBQpqX2DDy/8qZ67gMHDkRoaCiGDRuGTZs2yRVm7t27h2PHjsl6j+jo6GD06NFIS0vD06dPm5RHlZs3b8LY2LjWGBMTEwBQKGIIBAKsWLEClZWV9Wq2SkRERERE1FLYg+M1IRAIIJFIsH//fgwbNgxlZWUoKyuDtrY27OzskJSUhFmzZik0iezXrx8CAgIQExODGTNmwMbGRun8vr6+8PLyQk5ODnJzc5GXl4eMjAxkZGRg2rRp+Oijjxqcc1BQEBwdHVFRUYFTp05h/fr1GDduHJYsWQJ1dcWvpo+Pj9KnvgBAx44dAUDhlpIq8fHx+PTTT+WOTZw4Ue6pKS9qiT0DzxugWlhYQCKR4MiRI9i5cyf8/PwwZ84chdi0tDRIpVI4Ozvj0aNHAIARI0YgMTERBw4cwKRJkxqVQ3WVlZVK3+/qqsaVvb/du3dHSEgIli1bhoSEBHh7ezc5JyIiIiIiooZigaOVKyoqgra2Njp06IDMzEyUlJQgNTUVqampCrGZmZlKm4E6ODggJiZGru+GMrq6unBzc5M94vXatWsIDQ3F9u3b4enpiZ49ezYod2NjY4jFYgDPe4J06tQJoaGhUFdXx5IlSxTiDQ0NZfE1qXqax4tXGowYMQJWVlayv2fMmFGvHJt7zwBgamoq24eNjQ20tbURHR0NHR0dvP/++3KxqampqKiowKhRoxTmiY+Pb5YCR7du3eq8vaSgoEAWq8zkyZORnp6OqKgohUfwEhERERERvQy8RaUVk0qlyMvLg62tLdTU1JCcnIxu3bph165dCq8OHTo0qm9DeXk5nJ2dsXHjRoUxU1NThIaGAnje+6OpvLy84OTkhD179iA3N7dRc4jFYnTp0gXp6elyx/X19SEWi2Wv2q5YeJl7BoAPP/wQIpEIa9eulZvz/PnzuHTpEoKCghQ+T19fX/z666/49ddfm7y+i4sLCgoKcPHixRpj0tPT0aZNG9jZ2SkdFwgEiIiIQGVlJRYvXtzknIiIiIiIiBqKBY5WLD4+HsXFxfDx8UFJSQmys7MxZswY2RNUqr9Gjx6NEydOyP5LfH1paGigc+fOSE5OxsOHDxXGr127BoFAAHNz82bZU3h4ODQ1NbF8+XKUl5c3+HyhUIg5c+YgNzcXu3btUhpz69YtPHnypMY5Xvaeq65YKS8vx4oVK2THk5OToa2tDX9/f4XPc9q0aRAKhc3SbNTLywvdunVDeHg4Hj9+rDCekZGBQ4cO4d1331Xoi1Jd9+7dsWDBAuTk5CA7O7vJeRERERERETUEb1FpBUpLS3Hu3DkAz5/Acf/+feTk5CAhIQHjx4+Hm5sbtm3bBolEgjFjxiidw8PDA3v37kViYiKCg4MbtP7ixYvh7+8PDw8P+Pn5wcLCAhUVFcjLy0NsbCymTJkCU1PTJu8TeN7M8t1338V///tf7Nq1S+6WjcLCQtn78CIdHR2IRCIAwNtvv40bN24gIiICWVlZGDduHN544w08fPgQ2dnZSEtLg7a2NoYOHfpK7BkABgwYgLFjx+LAgQNIT0+Hs7MzDh06BGdnZ6WPzTUyMoK9vT0OHjyIjz/+uEmP1tXT00N0dDRmzpyJCRMmICAgABYWFnjy5Am+++47pKamYtSoUfjwww/rnGvq1Kk4fPgwTp8+DQMDg0bnRERERERE1FAscLQCFy9elDVuFAqF6NSpE0xNTbFq1SqMGzcOALBv3z6Ym5vX+OhTa2trmJmZISUlBYGBgdDU1Kz3+tbW1khNTcXWrVsRFxeHO3fuQE1NDebm5ggLC6ux8WdjzZw5E2lpadi4cSPGjRsHfX19AEBCQgISEhKUnmNhYYG0tDTZ3yEhIXBxcUFCQgKio6NRXFwMbW1tmJubIygoCBMnTkT79u1fmT0DwMKFC3H06FFERUXh2bNnePz4sezzVcbd3R0nTpxAWloapkyZ0qS1LS0t8c0332DXrl2Ij4/H7du3oaOjgz59+uCLL77AyJEj6zVP1a0q48ePb1I+REREREREDSWorOmxE0RELcTV1RVXr17F307zoGXUS9XpEBG9MsoKL6Mwdh7Onj0LW1tbVadDRETU4lxdXQEA33//fZPn4hUc1GwkEkmdMUKhEEIhW78QERERERFR82KBg5qFRCKBpaVlnXH29vbYuXNnyydERERERERE/ygscFCzUFdXR3Jycp1xTWmGSURERERERFQTFjio2YjFYlWnQERERERERP9QLHAQkcqU3y1QdQpERK8U/rtIRETUeCxwEJFKCIVC3D2wRtVpEBG9crR1dGFgYKDqNIiIiFodFjiISCWMjY2RlJSk6jSIiF45BgYGMDExUXUaRERErQ4LHESkEurq6rC1tVV1GkRERERE9JoQqjoBIiIiIiIiIqKmYoGDiIiIiIiIiFo9FjiIiIiIiIiIqNVjDw4iUgmJRIIff/xR1WkQEdUbm38SERG92ljgICKVuHnzJvr376/qNIiI6k1bRxf/91s+ixxERESvKBY4iEglKioq0GlsMDQ6dVd1KkREdSq/W4C7B9agpKSEBQ4iIqJXFAscRKQyGp26Q8uol6rTICIiIiKi1wCbjBIRERERERFRq8cCBxERERERERG1eixwEBEREREREVGrxwIHEREREREREbV6LHAQERERERERUavHAgcRERERERERtXqvZIHD19cXIpFI9rKwsICNjQ08PT0RFxcHqVSq9LyFCxdCJBJh69atCmOrV6+GSCRCSkqK0nPz8/NhaWmJBQsWyI7dvHkToaGhcHJygpWVFezt7TFz5kzk5eU1eE/Xr1+HSCRCWlpavc955513IBKJcPjw4RpjfvvtN8ybNw+DBw+GlZUVhgwZgvnz5+O3336Txaxdu1bu/VT26tOnT73zkkgkCudbWFigb9++GDt2LGJiYlBZWSmL9/HxQUBAQI3zOTo6IiwsTOH4mTNnMHv2bAwaNAhisRiurq5YsmQJrly5onSOAQMGoLCwsMZ8N23aJJdTbe/H22+/Xe/3o7qHDx9iw4YNGDduHGxsbGBvbw9/f39kZmbKxdX1fTh58iREIhHOnDmjMPb48WP07dsXVlZWuHPnTo37dXFxwV9//aUwXtvaOTk5CAoKgrOzs9x3Pjs7W+katb1Wr15d63tFRERERETUnNRVnUBN+vTpg6VLlwIApFIpHj58iKysLERERODs2bNYu3YtBAKBLL60tBQZGRno3bs3EhMTMX36dLnxoKAgHD16FFFRUXBycoKBgYFsTCqVIiwsDPr6+rI1i4qK4O3tDSMjIyxYsABdu3bFvXv3kJiYCH9/f0RHR2PYsGEttv+rV6/ip59+Qu/evREfH48RI0YoxPz222/w8fFB3759ERYWBgMDAxQWFmLXrl3w9vZGXFwcrK2t4ePjg6FDh8rOS0hIQFpaGvbu3Ss7Vv29qi9vb294enrK/n7y5AnS09MRFRWFR48eYd68eQ2es8rmzZuxbt06ODg4yPZ248YN7NmzB56enli1ahVGjRold87jx4+xePFibNu2rV5riMVihIeHKx1r06ZNg3O+fPkypk+fDgDw8/ODSCTCkydP8O2332LmzJkICgrC7NmzGzzviw4cOIC2bdtCKpUiOTkZs2bNUhp369YtfPbZZ/jPf/5Tr3lXrFiBuLg4jBgxAgsXLoShoSGKi4uRlpaGadOm4eOPP8a7774rd86L34HqunTp0rCNERERERERNcErW+DQ09NDv3795I65uLjA1NQUkZGRcHFxwfjx42VjBw8ehFQqRXh4OPz8/JCTkwMHBwfZuKamJiIjI+Hj44Ply5fjyy+/lI3t2LEDFy5cwLZt29C+fXsAz4sAf/31F2JjY6GnpyeLHT58OLy8vLB+/foWLXAkJyejW7dumDFjBkJCQnD9+nX06NFDLuarr76Cvr4+tm/fDnX1//9Ruri4YOTIkdi8eTM2b94MIyMjGBkZycarriZ48f1tKCMjI4U5Bg0ahKtXr2L37t0IDAyEmppag+f97rvvsG7dOgQGBmLOnDmy43Z2dpgwYQLmz5+Pjz/+GL1790bPnj1l4+3atcPx48eRkpICLy+vOtdR9h1rrGfPnmHu3LnQ0tLC3r17oa+vLxsbNmwYlixZgvXr18PV1RUWFhZNWislJQUODg7Q0NBAUlISZsyYAaFQ8WKsdu3aISEhASNHjoS9vX2tcyYlJSEuLg6ffPKJwtU2o0ePxtKlS7FmzRqMGjVK7ruk7DtARERERESkCq/kLSq18fX1haGhIeLj4+WOp6SkwM7ODnZ2djA1NVUYB4C+ffsiICAA6enpsh/5169fx4YNG+Dj4wNHR0dZbElJCQQCgdytFgCgpqaG4ODgRt/CUB8SiQT79+/H0KFD4erqCl1dXaX7KSkpAQCFHPX09BAaGqr0qo+XwcrKCo8fP8ajR48adf6GDRvQq1cvueJGFU1NTSxfvhyVlZXYvn273Njw4cMxYMAArFq1CkVFRY1au7GOHj2Ky5cvY8GCBXLFjSqBgYGYOnUqJBJJk9b5/fff8csvv8DZ2Rnjx4/HrVu3cPz4caWxkydPhomJCcLCwpTeqlKlsrISmzdvho2NDfz9/ZXGBAYGYsiQIXjw4EGT8iciIiIiImopra7AoaamBnt7e5w/f172Y/HKlSv4+eef4eHhAQDw9PREZmam0h+5c+fOhZmZGVasWIGysjIsW7YMRkZGWLhwoVycs7Mznjx5gkmTJiEmJgb5+fmoqKgAADg4OGDq1KkttsesrCzcuXMH7u7u0NHRwahRo7Bv3z48e/ZMIcebN2/inXfewZ49e+R6U4wePRru7u4tlmNtrl27hnbt2qFjx46yY5WVlZBIJEpf1ZWUlCA/Px8uLi41zq+vrw97e3scPXpU7rhAIEBERASePXuGxYsX15lnbTm9WDSqS3Z2NtTV1eWuGqquc+fOWLx4MaysrOqVQ9V37UXJycno0KEDhg4digEDBqBHjx5Ki18AoKWlhYiICNy+fbvWfhgXL17ErVu3MGbMmBpvVTIwMMCWLVsUrj6pqKio1+dKRERERETU0lpdgQN4/mOrvLxc9l+Tk5OT0a5dO9ktI1U/7JOSkhTO1dLSQmRkJG7fvo1p06YhNzcXUVFR0NXVlYtzcXFBeHg4iouLERUVBXd3dwwcOBCBgYE4ceJEi+4vJSUFvXv3hlgsBgB4eXnh/v37SE9Pl4ubOnUqZs6ciUuXLmHZsmUYPXo07O3t8dFHH+H8+fMtmiMg/+P22bNnuH37NrZs2YJjx45h8uTJcrE//PADLC0tlb6qF6Ju3rwJADA2Nq51bRMTEzx48AClpaVyx3v06IH58+cjKysLqamptc5RW06HDh1qyFuBwsJCdOrUCTo6Og06b9GiRUrXf//99xViy8vLsX//fowdOxaampoAnhfzsrKycPv2baXzDxgwAFOnTsXXX3+NH374QWnMjRs3AABvvvmm3HFlxZcXCy/R0dE1vofKGqASERERERG1lFe2B0d9CAQC2e0cw4YNQ1lZGcrKyqCtrQ07OzskJSVh1qxZCn0g+vXrh4CAAMTExGDGjBmwsbFROr+vry+8vLyQk5OD3Nxc5OXlISMjAxkZGZg2bRo++uijZt/T3bt3cfz4ccycOVN2i0evXr1gYmKC+Ph4ub4jAoEA8+fPx3vvvYfjx48jNzcXp06dwv79+/Htt99i8eLFmDJlSrPnWCU6OhrR0dFyx7S1tTF58mSFZprW1tZYsmSJ0nlmzJgh+99VV05U7ymiTG3jfn5+OHz4MCIjIzF48GB06tRJaVxtOZmYmNS6/ouEQmGNV13UJigoSO7WqCrnz5/HsmXL5I5lZmbi3r17GDZsmOy7MWzYMKxbtw6JiYk1NnUNDg5GVlYWwsLC8O233yqM13S1Snx8PD799FO5YxMnTsTKlStlf/v4+NTY76T6FTxEREREREQtrVUWOIqKiqCtrY0OHTogMzMTJSUlSE1NVfpf7DMzinOMRAAAIABJREFUM5U2A3VwcEBMTIzSH5fV6erqws3NDW5ubgCe334RGhqK7du3w9PTU67JZXP45ptvUF5errR4cOPGDfz+++8wNzeXO96+fXuMGzcO48aNAwD8+uuv+OijjxAVFYWxY8fKGqc2t+o/boVCIXR1dWFsbAwNDQ2F2DZt2siuSHlR9WJFt27dADx/AkhtCgoK0K5dO7kGsFWEQiEiIiLg7u6OpUuXYsOGDUrnqC2nhurWrRtOnjyJp0+f1ngVx59//omuXbvKHTM2Nlaaw+PHjxWOVT3iWNkjd5OTkzFnzhylhR8dHR1ERETA19cXq1evVuizUdN7PmLECLlbaqoXoqoYGho223tIRERERETUFK3uFhWpVIq8vDzY2tpCTU1N9rSRXbt2Kbw6dOhQY3+C2pSXl8PZ2RkbN25UGDM1NUVoaCgAyPW8aC6pqamwsbFR2MuWLVsgFApl+7l16xaGDBmCffv2KcxhZWWFuXPnoqysTHbLR0uo+nErFothaWkJU1NTpcWNhs5pZWWFI0eO1HhlwaNHj3Dy5Em4urrWOI+pqSnmzp2LzMxMfPPNN03KqT6GDBkCiURS4+1LVVdevHhVRn0VFxcjOzsbkydPVvhuLFiwAHfu3MH3339f4/n//ve/MWXKFOzduxd5eXlyY2KxGF26dFG4BUpfX1/2+YrF4jqvqiEiIiIiIlKlVlfgiI+PR3FxMXx8fFBSUoLs7GyMGTNG9gSV6q/Ro0fjxIkTKCgoaNAaGhoa6Ny5M5KTk/Hw4UOF8WvXrkEgEChcSdFU586dw+XLl+Hl5aWwl6FDh2LQoEFIS0vD06dP0aVLFwiFQuzevVuh+WhVjtra2g2+1eJVEBQUhEuXLilcwQI8L3AtWbIEEokE7733Xq3zBAQEwMbGBqtWrWqpVGWcnJzQq1cvrFmzRumTRtasWQOJRNLoxq/ffPMNpFIpAgICFL4bAQEBaNu2bZ3FvODgYBgbGyMqKkruuFAoxJw5c5Cbm4tdu3YpPffWrVt48uRJo3InIiIiIiJ6GV7Z/yRbWlqKc+fOAXjezPL+/fvIyclBQkICxo8fDzc3N2zbtg0SiQRjxoxROoeHhwf27t2LxMREBAcHN2j9xYsXw9/fHx4eHvDz84OFhQUqKiqQl5eH2NhYTJkyBaampg3eV05ODu7fv69wfNSoUUhJSYGGhkaNj3d1d3dHTk4ODhw4gEmTJmHp0qUIDAyEl5cXpkyZAjMzMzx9+hTZ2dn4+uuvERwcjLZt2zY4R1VzcnJCSEgIvvjiC1y4cAETJkyAoaEhCgoKsHfvXly6dAkRERHo3bt3rfNUv1VFmerfMWXEYrFC/5aaaGhoICoqCu+//z48PT1l35l79+4hNTUV2dnZCA4OhrW1db3me1FqairEYjF69OihMKalpYVRo0YhKSkJ169fl91y8iJdXV2sXLlS6aNg3377bdy4cQMRERHIysrCuHHj8MYbb+Dhw4fIzs5GWloatLW1MXToULnzCgsLa3wPdXR0IBKJGrFbIiIiIiKihntlCxwXL16Et7c3gOc/VDt16gRTU1OsWrVK1mti3759MDc3V3h0ZRVra2uYmZkhJSUFgYGBsidP1Ie1tTVSU1OxdetWxMXF4c6dO1BTU4O5uTnCwsJqbKxYl/3792P//v0Kx62srHDo0CE4OjqiXbt2Ss8dPnw49PT0EB8fj0mTJsHV1RUJCQnYsWMHNm/ejLt370JLSwuWlpb48ssvlfYeaS2mT5+OAQMGIDY2FlFRUbh37x4MDQ0xePBgrFq1qt69T8zMzBAUFITPP/9cYeyXX36RfceUOX36dI2fhTJWVlZISUnBV199ha+//hpFRUXQ1dWFSCTC9u3ba3yEbF3Onj2La9eu4ZNPPqkxxt3dHYmJiYiPj6+1mGdnZwcfHx/s3btXYSwkJAQuLi5ISEhAdHQ0iouLoa2tDXNzcwQFBWHixIkK/VwSEhKQkJCgdC0LCwukpaXVc5dERERERERNI6isqdEBEVELcXV1xdWrV/G30zxoGfVSdTpERHUqK7yMwth5OHv2LGxtbVWdDhER0WujqrdibT0F6+uVvYKjtZBIJHXGCIVCCIWvfrsTqVRaY2PPKgKBoN63bbR2lZWVkEqldcapqalBIBC8hIyIiIiIiIioJixwNIFEIoGlpWWdcfb29ti5c2fLJ9REQ4cORVFRUa0xJiYmOHLkyEvKSLVyc3Px7rvv1hk3d+5cfPjhhy8hIyIiIiIiIqoJCxxNoK6ujuTk5Drj9PT0XkI2Tbd161aUl5fXGqOlpfWSslE9a2vren2+Xbp0eQnZEBERERERUW1Y4GgisVis6hSaTU3NWv+p9PT0XqvPl4iIiIiI6HX26jeGICIiIiIiIiKqA6/gICKVKb9boOoUiIjqhf9eERERvfpY4CAilRAKhbh7YI2q0yAiqjdtHV0YGBioOg0iIiKqAQscRKQSxsbGSEpKUnUaRET1ZmBgABMTE1WnQURERDVggYOIVEJdXR22traqToOIiIiIiF4TbDJKRERERERERK0eCxxERERERERE1OqxwEFERERERERErR57cBCRSkgkEvz444+qToOISIZNRImIiFo3FjiISCVu3ryJ/v37qzoNIiIZbR1d/N9v+SxyEBERtVIscBCRSlRUVKDT2GBodOqu6lSIiFB+twB3D6xBSUkJCxxEREStFAscRKQyGp26Q8uol6rTICIiIiKi1wCbjBIRERERERFRq8cCBxERERERERG1eixwEBEREREREVGrxwIHEREREREREbV6LHAQERERERERUavHAgcRERERERERtXoscJCMr68vRCKR7GVhYQEbGxt4enoiLi4OUqlU6XkLFy6ESCTC1q1bFcZWr14NkUiElJQUpefm5+fD0tISCxYskB27efMmQkND4eTkBCsrK9jb22PmzJnIy8tr1L4cHR0RFhYm+zskJERhn/369cO4ceOwefNmlJWVNXiNkydPys0pEonwr3/9C7a2tvDx8cGxY8dksdevX4dIJEJaWlqtc505c0YuvvrL0tISTk5OWLp0KR48eCA7VyKRQCQSYdOmTbXmu3btWvTp00fuWHl5Ob766iu4u7ujb9++ss8+JiYGz549q/cade2PiIiIiIioJairOgF6tfTp0wdLly4FAEilUjx8+BBZWVmIiIjA2bNnsXbtWggEAll8aWkpMjIy0Lt3byQmJmL69Oly40FBQTh69CiioqLg5OQEAwMD2ZhUKkVYWBj09fVlaxYVFcHb2xtGRkZYsGABunbtinv37iExMRH+/v6Ijo7GsGHDmrxPIyMjfPnllwCAiooKPH78GKdOncLGjRtx8uRJ7NixA5qamg2ed9myZRCJRACAyspKPHjwADExMZg5cyZ27NiBwYMHNzrnwMBADBkyBABQVlaGK1euYMOGDbh69Sri4uIaPW+V0NBQHD16FNOnT4dYLIZUKsWZM2ewdu1anDt3DuvXr2/yGkRERERERC2FBQ6So6enh379+skdc3FxgampKSIjI+Hi4oLx48fLxg4ePAipVIrw8HD4+fkhJycHDg4OsnFNTU1ERkbCx8cHy5cvlxUVAGDHjh24cOECtm3bhvbt2wMAEhIS8NdffyE2NhZ6enqy2OHDh8PLywvr169vlgKHpqamwj6dnJwgFosxb9487Ny5Ex988EGD5+3Zs6fCvP/+97/h6OiIXbt2NanA0b17d7m57ezsoK6ujsWLF+Pq1aswMzNr9NwFBQXYv38/Vq5ciYkTJ8qOOzo6okOHDoiKisLFixcVrvogIiIiIiJ6VfAWFaoXX19fGBoaIj4+Xu54SkoK7OzsYGdnB1NTU4VxAOjbty8CAgKQnp6OzMxMAM9vY9iwYQN8fHzg6Ogoiy0pKYFAIEBlZaXcHGpqaggODsbbb7/dArv7/0aNGgVLS0ul+2gsPT09vPnmm7h9+3azzVmlqjBU/aqZxigpKQEAhfcdAMaPH4/g4GC5ghMREREREdGrhgUOqhc1NTXY29vj/PnzkEgkAIArV67g559/hoeHBwDA09MTmZmZKCoqUjh/7ty5MDMzw4oVK1BWVoZly5bByMgICxculItzdnbGkydPMGnSJMTExCA/Px8VFRUAAAcHB0ydOrWFdwoMGTIEt27dUrqPxnj27Blu3bqF7t27N2meyspKSCQSSCQS/P3338jPz8eWLVswePBgmJqaNmnuPn36wNDQECtWrMCyZcuQnZ2N0tJSAICBgQE++OADmJiYyJ1TUVEhy6f6q6ZeLURERERERC2JBQ6qNwMDA5SXl8uaWiYnJ6Ndu3ayW0bc3d0BAElJSQrnamlpITIyErdv38a0adOQm5uLqKgo6OrqysW5uLggPDwcxcXFiIqKgru7OwYOHIjAwECcOHGihXf4XFWfkDt37jT43Oo/+v/++29cvnwZCxcuxIMHDzBlypQm5bVo0SJYWlrC0tISffv2hbu7O/7880+5BqqNpaWlhW3btqFHjx7Ys2cPpk2bhoEDB8oKTcoar0ZHR8vyqf4aNWpUk/MhIiIiIiJqKPbgoAYTCASQSCTYv38/hg0bhrKyMpSVlUFbWxt2dnZISkrCrFmzoKamJndev379EBAQgJiYGMyYMQM2NjZK5/f19YWXlxdycnKQm5uLvLw8ZGRkICMjA9OmTcNHH330MrbZqNs+fH19FY4ZGBhgyZIlsv4b9Z33xbigoCDZ7TwSiQR//vkndu7cCW9vb+zevRsWFhYNzrc6CwsL7N+/H+fPn0dOTg5OnTqFn376CefPn0dKSgp2796Njh07yuJ9fHzg5eWlME9RURFmz57dpFyIiIiIiIgaigUOqreioiJoa2ujQ4cOyMzMRElJCVJTU5GamqoQm5mZqbQZqIODA2JiYuT6biijq6sLNzc3uLm5AQCuXbuG0NBQbN++HZ6enujZs2fzbEqJqltTjIyMGnzuihUrZIUGNTU1tG/fHm+88YZcsUJHRwcA5B69Wl3V8RevbjE2NoZYLJb9bWNjA0dHRzg5OWHDhg3YsGFDg/NVxtraGtbW1vjwww/x9OlT7NixA9HR0dixYwdCQkJkcYaGhnL5VGnXrl2z5EFERERERNQQvEWF6kUqlSIvLw+2trZQU1NDcnIyunXrhl27dim8OnTo0KgmneXl5XB2dsbGjRsVxkxNTREaGgrgee+PlpSbmwszMzN06tSpweeamppCLBZDLBajT58+6Natm8KVGO3bt4eGhgaKi4uVzlFYWAgAco/UrYmenh66d++O69evNzjX6lauXIkxY8YoHNfR0cGcOXNgbm6Oy5cvN2kNIiIiIiKilsQCB9VLfHw8iouL4ePjg5KSEmRnZ2PMmDGyJ6hUf40ePRonTpxAQUFBg9bQ0NBA586dkZycjIcPHyqMX7t2DQKBAObm5s21LQXfffcdLly4AB8fnxZbQ1NTEzY2Njhy5IjShpyHDx9Gz5490blz5zrnevToEa5evYoePXo0KSdTU1NcvnwZhw8fVhgrLS3FnTt3IBKJmrQGERERERFRS+ItKiSntLQU586dA/C8Yeb9+/eRk5ODhIQEjB8/Hm5ubti2bRskEonS/+IPAB4eHti7dy8SExMRHBzcoPUXL14Mf39/eHh4wM/PDxYWFqioqEBeXh5iY2MxZcqUJj8xBHh+G0jVPisrK/Ho0SPk5eUhLi4OgwYNwuTJk5u8Rm3mzp2LgIAA+Pv7Y/LkyTA0NMS9e/eQlpaG06dPY9OmTQrnFBQUyHIGnj/adevWrZBKpXjvvffkYs+dO4edO3cqzOHg4KD09p6JEyfiwIEDCA4Oxg8//ABHR0e0bdsW165dw65du9CmTRv4+/s3feNEREREREQthAUOknPx4kV4e3sDAIRCITp16gRTU1OsWrUK48aNAwDs27cP5ubmNTa1tLa2hpmZGVJSUhAYGAhNTc16r29tbY3U1FRs3boVcXFxuHPnDtTU1GBubo6wsDClTS0bo7CwULZPgUCAjh07wsTEBJ988gm8vLygrt6y/9cYMGAAvv76a2zbtg0RERF48OABOnTogL59+2L37t3o16+fwjnR0dGIjo4G8PyzadeuHaysrBATEwNbW1u52KysLGRlZSnM0bFjR6UFDk1NTXz11VeIjY3F4cOH8e233+Lvv/9Gly5d4OLiglmzZkFfX7+Zdk9ERERERNT8BJWVlZWqToKI/llcXV1x9epV/O00D1pGvVSdDhERygovozB2Hs6ePatQNCYiIqKW4+rqCgD4/vvvmzwXr+CgVkkikdQZIxQKIRQ2rc3My1qHiIiIiIiImoYFDmp1JBIJLC0t64yzt7dX2oeivq5fvy57TG1tJk6ciJUrVzZ6HSIiIiIiImo6Fjio1VFXV0dycnKdcXp6ek1ap2vXrvVah70piIiIiIiIVI8FDmqVxGJxi6+hqan5UtYhIiIiIiKipmPjACIiIiIiIiJq9XgFBxGpTPndAlWnQEQEgP8eERERvQ5Y4CAilRAKhbh7YI2q0yAiktHW0YWBgYGq0yAiIqJGYoGDiFTC2NgYSUlJqk6DiEjGwMAAJiYmqk6DiIiIGokFDiJSCXV1ddja2qo6DSIiIiIiek2wySgRERERERERtXoscBARERERERFRq8cCBxERERERERG1euzBQUQqIZFI8OOPP6o6DSL6B2DzUCIion8GFjiISCVu3ryJ/v37qzoNIvoH0NbRxf/9ls8iBxER0WuOBQ4iUomKigp0GhsMjU7dVZ0KEb3Gyu8W4O6BNSgpKWGBg4iI6DXHAgcRqYxGp+7QMuql6jSIiIiIiOg1wCajRERERERERNTqscBBRERERERERK0eCxxERERERERE1OqxwEFERERERERErR4LHERERERERETU6rHAQUREREREREStHgscVCtfX1+IRCLZy8LCAjY2NvD09ERcXBykUqnS8xYuXAiRSIStW7cqjK1evRoikQgpKSlKz83Pz4elpSUWLFggO3bz5k2EhobCyckJVlZWsLe3x8yZM5GXl9fgPUkkErk9KXutXr1aIfbLL79UOp9UKsXgwYMhEomQlpYGALh+/brCnNXfu3379inks2nTpnrlf+bMGcyePRuDBg2CWCyGq6srlixZgitXrshiKioq8M4770AsFssdr2737t0QiUSIj48HAPj4+NT6nrz99tuyc1+Mrb63PXv2oKKiol57ISIiIiIiai7qqk6AXn19+vTB0qVLATz/Mf/w4UNkZWUhIiICZ8+exdq1ayEQCGTxpaWlyMjIQO/evZGYmIjp06fLjQcFBeHo0aOIioqCk5MTDAwMZGNSqRRhYWHQ19eXrVlUVARvb28YGRlhwYIF6Nq1K+7du4fExET4+/sjOjoaw4YNa/C+vL294enpqXSsS5cucn8LhUL873//w9y5cxViT506hZKSEqXzBAYGYsiQIQCAyspKlJaWIjExER9//DEkEgkmTZrUoJw3b96MdevWwcHBAWFhYTAwMMCNGzewZ88eeHp6YtWqVRg1ahSEQiEiIyPh7u6O8PBw7N27V+4zuH37NtasWQNHR0e88847suNisRjh4eFK127Tpo3c39Vjq74Xx44dw/Lly/Hjjz9i9erVcmsSERERERG1JBY4qE56enro16+f3DEXFxeYmpoiMjISLi4uGD9+vGzs4MGDkEqlCA8Ph5+fH3JycuDg4CAb19TURGRkJHx8fLB8+XK5KyN27NiBCxcuYNu2bWjfvj0AICEhAX/99RdiY2Ohp6cnix0+fDi8vLywfv36RhU4jIyMFPZVE1tbW5w5cwa//fYbLCws5MYOHjyIf/3rX8jPz1c4r3v37gprDBo0CG5uboiNjW1QgeO7777DunXrEBgYiDlz5siO29nZYcKECZg/fz4+/vhj9O7dGz179oSpqSmCgoLw2WefYe/evZgyZYrsnCVLlkBTUxMrV66UW0PZZ12Tmr4XJiYm+Pzzz+Hq6orRo0fXe39ERERERERNwVtUqNF8fX1haGgou8WhSkpKCuzs7GBnZwdTU1OFcQDo27cvAgICkJ6ejszMTADPb+vYsGEDfHx84OjoKIstKSmBQCBAZWWl3BxqamoIDg6Wu3Wipbz11lvo2LEj/ve//8kdLy8vx3fffYcxY8bUey41NTX06dMHt27dalAOGzZsQK9eveSKG1U0NTWxfPlyVFZWYvv27bLj7777LmxsbPDFF1+gqKgIAJCWlobs7GwsXboUhoaGDcqhPgICAmBgYICvv/662ecmIiIiIiKqCQsc1Ghqamqwt7fH+fPnIZFIAABXrlzBzz//DA8PDwCAp6cnMjMzZT+uq5s7dy7MzMywYsUKlJWVYdmyZTAyMsLChQvl4pydnfHkyRNMmjQJMTExyM/Pl/V4cHBwwNSpUxuVf0VFBSQSidKXsr26ubkhPT1d7viJEycgkUjg5OTUoLX/+OMPmJiY1Du+pKQE+fn5cHFxqTFGX18f9vb2OHr0qOyYUCjEypUrUV5ejs8++wyPHj1CVFQUxo4dq/TqisrKyhrfkxcLTDVRV1fHW2+9hZ9++om9OIiIiIiI6KVhgYOaxMDAAOXl5Xjw4AEAIDk5Ge3atZPdMuLu7g4ASEpKUjhXS0sLkZGRuH37NqZNm4bc3FxERUVBV1dXLs7FxQXh4eEoLi5GVFQU3N3dMXDgQAQGBuLEiRONzj06OhqWlpZKX3fu3FGIHz16NP744w/89ttvsmMHDx7E8OHDoaWlpXSN6gWDZ8+e4caNG1ixYgUuXbokd8tIXW7evAkAMDY2rjXOxMQEDx48QGlpqexYz549ERgYiAMHDuDDDz+EmpoalixZovT8H374ocb35NChQ/XOt+p78fDhw3qfQ0RERERE1BTswUHNQiAQQCKRYP/+/Rg2bBjKyspQVlYGbW1t2NnZISkpCbNmzYKamprcef369UNAQABiYmIwY8YM2NjYKJ3f19cXXl5eyMnJQW5uLvLy8pCRkYGMjAxMmzYNH330UYNz9vHxgZeXl9Kxjh07KhwbOHAgOnfujP/973+wsLBAWVkZvv/++xqfrgIAixYtwqJFi+SOtW3bFrNnz25Q/42qqyfU1Wv/v2xN4++99x4yMjJw+vRpbN++Xdbf5EXW1tY1Fj8acsVJFTYZJSIiIiKil4UFDmqSoqIiaGtro0OHDsjMzERJSQlSU1ORmpqqEJuZmam0GaiDgwNiYmLk+m4oo6urCzc3N7i5uQEArl27htDQUGzfvh2enp7o2bNng3I3NDSEWCyud7xQKMSIESOQnp6O+fPnIysrC5qamrC3t6+xn0ZQUJBsX0KhEG3btoWxsTGEwoZdPNWtWzcAqLNvR0FBAdq1ayfXjBV4fovNoEGDcOHCBbmGry9q06ZNg96TmhQVFUFXVxft2rVr8lxERERERET1wVtUqNGkUiny8vJga2sLNTU1JCcno1u3bti1a5fCq0OHDkqbjdalvLwczs7O2Lhxo8KYqakpQkNDATzv/fEyjBo1Cn/88Qfy8/Nx6NAhjBw5starKoyNjSEWiyEWi2FpaQkTE5MGFzeA58UYKysrHDlypMZeGI8ePcLJkyfh6ura4Pmbk0QiwenTp9G/f/9G7ZWIiIiIiKgx+OuDGi0+Ph7FxcXw8fFBSUkJsrOzMWbMGNkTVKq/Ro8ejRMnTqCgoKBBa2hoaKBz585ITk5W2s/h2rVrEAgEMDc3b65t1ap///4wMjJCWloajh07hrFjx76UdYHnV4NcunQJ0dHRCmNSqRRLliyBRCLBe++999JyUmbPnj0oKSmBj4+PSvMgIiIiIqJ/Ft6iQnUqLS3FuXPnADx/8sj9+/eRk5ODhIQEjB8/Hm5ubti2bRskEkmNj0v18PDA3r17kZiYiODg4Aatv3jxYvj7+8PDwwN+fn6wsLBARUUF8vLyEBsbiylTpsDU1LTB+yosLJTt60U6OjoQiUQKxwUCAUaOHIndu3ejU6dO6N+/f4PXrcm5c+ewc+dOheMODg7o2bMnnJycEBISgi+++AIXLlzAhAkTYGhoiIKCAuzduxeXLl1CREQEevfu3egcqn/WyojFYlkfFWXfi+PHjyMxMREeHh4qv5KEiIiIiIj+WVjgoDpdvHgR3t7eAJ73kejUqRNMTU2xatUqjBs3DgCwb98+mJubw8LCQukc1tbWMDMzQ0pKCgIDA6GpqVnv9a2trZGamoqtW7ciLi4Od+7cgZqaGszNzREWFlZjo9C6JCQkICEhQemYhYUF0tLSlI6NHj0aO3fuxOjRo5u1iWZWVhaysrIUjnfs2FHWX2T69OkYMGAAYmNjERUVhXv37sHQ0BCDBw/GqlWrGtyH5EW//PKL7LNW5vTp07K+GtVjq74XZmZm+Pzzz2ssdBEREREREbUUQWVNN/QTEbUQV1dXXL16FX87zYOWUS9Vp0NEr7GywssojJ2Hs2fPwtbWVtXpEBER0Quqrvz+/vvvmzwXr+Cg14ZEIqkzRigUsvElERERERHRa4gFDnotSCQSWFpa1hlnb2+vtM8FERERERERtW4scNBrQV1dHcnJyXXG6enpvYRsiIiIiIiI6GVjgYNeG2KxWNUpEBERERERkYqwGQERERERERERtXq8goOIVKb8boGqUyCi1xz/nSGi/8fefYdFcbX9A//Si4oEERtibAsRUFCKgijNoFhBIhhBiL0FSxJ76qNiYu8+0SSW4INSVTS2iCgae43ltSuoIGAhKHWZ3x/+duO6S5OFZfX7uS6uvDvnnjP3mT34PnMzc4aI3h8scBCRSmhqaiI7cZGq0yCi94C+gSFMTU1VnQYRERFVMxY4iEglzM3NER0dreo0iOg9YGpqCgsLC1WnQURERNWMBQ4iUgltbW107NhR1WkQEREREdE7gouMEhEREREREZHaY4E664lBAAAgAElEQVSDiIiIiIiIiNQeCxxEREREREREpPa4BgcRqURxcTHOnj2r6jSI3klcVJOIiIjeRyxwEJFKpKWloVOnTqpOg+idpG9giP+7dpVFDiIiInqvsMBBRCpRUlKCBn2+gE6D5qpOheidUpSdiuzERcjKymKBg4iIiN4rLHAQkcroNGgOvcZtVJ0GERERERG9A7jIKBERERERERGpPRY4iIiIiIiIiEjtscBBRERERERERGqPBQ4iIiIiIiIiUnsscBARERERERGR2mOBg4iIiIiIiIjUHl8Tq+amTp2K7du344svvsCoUaNk2qZPn474+Hg0atQIycnJ0NDQkNt/4cKFWLduHZycnLB582bpPmVp1qwZDh48WOEcPT094eTkhPnz58vktWrVKnh7e8vFh4SEAAA2b94ss/3OnTvYuHEjUlJS8PjxY5iYmMDe3h6jR4+GlZWVXD9paWlYt26dNL5+/fqwsbFBcHAwunbtKhP7Nufq9f1KY2xsjBMnTpRzhv7N18vLq9y4iIgI+Pv7AwBKSkqwY8cOxMbG4urVqyguLoa5uTl8fHwQHByMDz74QLqfpaVluX1PmDABn3/+ucy2oKAgnDt3DsuXL4ePj4/cPoMHD4aenh42bNhQbv9ERERERETVhQUONZabm4t9+/ZBJBJh27ZtGDlypNyFuYaGBjIyMnDmzBk4ODjI9fHHH3/IfB43bhyCgoKkn1evXo0rV65g5cqV0m26urpKyf/bb7+Fg4MDjI2Ny43dv38/vvrqK7Rt2xZjx46Fubk50tPTsXnzZnzyySdYtWoVunXrJo3/66+/MH78eDRq1AjDhw9HmzZt8OTJEyQmJmL48OEIDQ3FzJkzZY5R2XMl0bBhQ5nz8zpt7Yr/ipmZmWHr1q3Sz5mZmZgwYQLGjh0Ld3d36XYLCwsAQFFRESZOnIjk5GT4+/tj2LBh0NfXx6VLl7Bp0ybExcVh7dq10sLG630DQGBgIAICAvDJJ59ItzVu3Fgm5vbt2zh37hxEIhGioqIUFjiIiIiIiIhqAxY41NiuXbsgFosxe/ZsDB06FCkpKXBzc5OJadKkCQBgz549chft58+fR3p6OkQikXSbhYWF9AIaAExMTKCrqws7Ozul5m5gYIDnz5/jP//5DxYtWlRm7P379zF16lS4ublh6dKl0NLSkrb5+Pjg008/xfTp03Hw4EHo6+sjIyMD4eHhsLe3x+rVq6GnpyeN79mzJzZs2ICIiAi0bdtW5uK+sudKQlnn581+0tLSALz6ThT1v3jxYiQnJ2Pt2rUy33uXLl3g5+eH4OBghIeHY/v27dDX11fYR+PGjcvMPSYmBs2aNcPo0aPx5Zdf4t69e2jRokVVhklERERERFQtuAaHGouNjYWzszOcnZ3RsmVLREVFycVoaGigZ8+e2Lt3L0pKSmTadu/eDRcXlwrdQaFsJiYmGDVqFBITE3HgwIEyYzdv3ozCwkLMnj1bprgBAPr6+pg2bRoCAgKQk5MDANiwYQNevHiBOXPmyBQ3JMLCwmBnZ4c1a9ZAEATp9tp6rhR5+vQpIiMj4e/vL1fUAl7dVTJr1izcvXsXiYmJb3WM4uJi7NixAx4eHvDy8oKhoaHCOUZERERERFQbsMChpm7duoULFy7Az88PAODv74+kpCRkZGTIxfr6+uLx48c4c+aMdFtJSQn27NmD3r1711jObxo7diwsLS3x3Xff4dmzZ6XGHTlyBO3atUOjRo0Utjs7O2PKlCkwMzMDAKSkpOCjjz6S3pGhSK9evfDgwQNcvXpVZvvbnqvi4mKFP68XUJTp5MmTKCgoKHPNjq5du8LY2Bh//vnnWx0jOTkZmZmZGDBgAAwMDNCrVy/Ex8ejsLDwbdMmIiIiIiKqNixwqKmYmBgYGRlJF+kcMGAAACA6Olou1tbWFhYWFtizZ4902+nTp/Hs2TOFi3zWFB0dHcyfPx9Pnz7FnDlzSo3LyMiAubl5hftNS0srN17ymMWDBw9ktr/NuXrw4AGsra0V/qxZs6bCeVeG5PGVssapqamJZs2ayY2xomJjYyESiWBrawsAGDhwIJ4+fSpzboiIiIiIiGoLFjjUkOTRAW9vbxQUFCAnJwf6+vpwdnZGdHQ0xGKx3D69evWSefRi165dcHd3R926dWs6fRnt2rXDyJEjsXPnzlLvNNDQ0FA4ptIIglDu4p6SR10U3WFR2XPVsGFDxMTEKPwJCAiocN6VIcm7vHFqa2u/1V0k2dnZOHz4MHx8fJCTk4OcnBy0adMGFhYWfEyFiIiIiIhqJRY41NChQ4eQlZWFuLg4ODo6Sn+OHTuG9PR0JCUlye3j6+uLzMxMnD59GmKxGPv27VPp4ymvGzduHEQiEb799ls8f/5crr1Zs2Z4+PBhqfsXFxfj8ePHMvHl3bWQmpoKAGjatKlcW2XPla6uLmxtbRX+SB6bUbZmzZoBkL8D5U2pqanS2MpISEhAUVERVqxYITPH7t+/jzNnzuDGjRtvlTcREREREVF1YYFDDUnebLFp0ya5H2NjY4V/YbeyskLLli2xZ88eHD9+HAUFBTKvHlUlXV1dRERE4OnTp5g7d65ce9euXXHlyhVkZmYq3P/IkSNwc3PDrl27AACenp64dOkSHj16VOox9+7diyZNmqBdu3ZybbX5XEl07doVurq62Lt3b6kxJ0+exJMnT8pcp6M0cXFxsLe3l5tfa9euhaamJu/iICIiIiKiWocFDjWTlZWFI0eOoHfv3tI3qLz+4+vri6NHj0rvUHidr68v9u/fj127dqFHjx4K3zCiKjY2NhgxYgS2b9+OK1euyLQNGTIEOjo6mDNnjtyjKnl5eVi+fDnq168PDw8PAEBISAjq1q2LGTNmoKCgQO5YW7ZswYkTJzB69Ghoair+FajN5woA6tWrh88++wwxMTE4fPiwXPvTp0/x/fffw8LCotJ36pw/fx43b97EwIED5eaXh4cHXFxcsH37duTl5SlrOERERERERFVW9gP8VOvEx8ejuLi41ItWPz8/bNmyBdu2bZNr8/X1xapVq7Bjxw6sXr26ulOttPHjx+PPP/+Ue/zB3Nwc3333HWbNmoUhQ4YgKCgITZo0wf3797Fhwwbcu3cP69atg6GhIQDAzMwMy5YtQ3h4OPz9/TF06FC0atUKz58/xx9//IFdu3ZhyJAhGDx4cKm5VOZcFRYW4vz586W2i0QiaW7K9Pnnn+PevXsYO3YsAgIC4OnpCQMDA1y5cgUbNmyAIAhYu3ZtpY8dGxsLHR0d+Pj4KGwfMGAAUlJSkJiYiE8++QQA8OjRI2zYsEEuViQSwcXFpdJjIyIiIiIiqiwWONRMfHw82rZtCysrK4Xt7du3R6tWrRAbG4suXbrItLVp0wYikQiZmZm18qJT8qhKYGCgXJufnx9atGiBjRs3YunSpcjOzkbDhg1hb2+PZcuWoU2bNjLxnTt3RkJCAjZs2IBff/0Vjx49gpGREWxtbbFu3Tq4ubmVmUtlzlVmZqbCnCViYmKkbyJRJh0dHSxbtgy7d+/G1q1bMW3aNOTn58Pc3Bz+/v4ICQnBBx98UKk+8/PzsXv3bnTr1g1GRkYKY3r06IG6desiKipKWuC4e/cuIiIi5GIDAgJq5VwjIiIiIqJ3j4bwNq9YICKqAi8vL9y+fRv53SdBr3Gb8ncgogorSL+J9I2TcObMGXTs2FHV6RARERGVSbJmYGlv1awM3sFBb6W4uLjcGA0NDenrWN93FTlfmpqapa4JQkRERERERGVjgYPeirW1dbkxTk5O2Lx5cw1kU7ulpaVV6E0mfn5+mD9/fg1kRERERERE9O5hgYPeSkxMTLkxderUqYFMaj8zM7MKna/KrpdBRERERERE/2KBg95KdSya+a7S1dXl+SIiIiIiIqpmfOCfiIiIiIiIiNQe7+AgIpUpyk5VdQpE7xz+XhEREdH7igUOIlIJTU1NZCcuUnUaRO8kfQNDmJqaqjoNIiIiohrFAgcRqYS5uTmio6NVnQbRO8nU1BQWFhaqToOIiIioRrHAQUQqoa2tjY4dO6o6DSIiIiIiekdwkVEiIiIiIiIiUnsscBARERERERGR2mOBg4iIiIiIiIjUHtfgICKVKC4uxtmzZ1WdBpHa44KiRERERK+wwEFEKpGWloZOnTqpOg0itadvYIj/u3aVRQ4iIiJ677HAQUQqUVJSggZ9voBOg+aqToVIbRVlpyI7cRGysrJY4CAiIqL3HgscRKQyOg2aQ69xG1WnQURERERE7wAuMkpEREREREREao8FDiIiIiIiIiJSeyxwEBEREREREZHaY4GDiIiIiIiIiNQeCxxEREREREREpPZY4CAiIiIiIiIitccCB9V6U6dOhaWlJX7++We5tunTp8PS0hLdunWDIAgK91+4cCEsLS0REhIis09ZP56enm+V64sXL2Bvb4927dohIyNDYYylpSXc3d2Rm5sr15aWlgZLS0vExcVVKX7FihUKj60oPiQkRHpu4uLiyj03lpaWuHnzJmxtbfHpp58qPO8lJSUICgqCo6Mj0tPTSzlbREREREREyqOt6gSIypKbm4t9+/ZBJBJh27ZtGDlyJDQ0NGRiNDQ0kJGRgTNnzsDBwUGujz/++EPm87hx4xAUFCT9vHr1aly5cgUrV66UbtPV1X2rfHfv3g19fX3UqVMH0dHRmDBhgsK4R48eYf78+ZgzZ06F+q1s/Ntyd3fH1q1bpZ8PHTqENWvWYOXKlWjYsKF0u4WFBcaPH48lS5Zgy5YtGDJkiEw/v//+O86dO4cFCxagcePG1ZozERERERERwAIH1XK7du2CWCzG7NmzMXToUKSkpMDNzU0mpkmTJgCAPXv2yBU4zp8/j/T0dIhEIuk2CwsLWFhYSD+bmJhAV1cXdnZ2Vc43NjYWXbt2Rd26dREdHY2xY8dCS0tLLs7IyAjR0dHo1asXXF1dy+23svFvy8TEBCYmJtLPt2/fBgB89NFHMDc3l4kdOXIk9u/fj8WLF8Pb2xuNGjUCADx48ABLliyBj48P+vXrV225EhERERERvY6PqFCtFhsbC2dnZzg7O6Nly5aIioqSi9HQ0EDPnj2xd+9elJSUyLTt3r0bLi4uMDY2rvZcb9++jXPnzsHDwwP9+vVDeno6kpKSFMYGBgaiZcuWmD17tsJHT6oaXxO0tLQQERGBgoICfPfdd9Lt33zzDerUqYPvv/9edckREREREdF7hwUOqrVu3bqFCxcuwM/PDwDg7++PpKQkhWtb+Pr64vHjxzhz5ox0W0lJCfbs2YPevXvXSL4xMTGoV68evLy8YG9vj1atWiksyACAnp4eIiIikJ6ejp9++qncvisbX1NEIhHGjRuHgwcPIjk5Gbt370ZKSgrmzp2LDz74QNXpERERERHRe4QFDqq1YmJiYGRkBG9vbwDAgAEDAADR0dFysba2trCwsMCePXuk206fPo1nz55J969OxcXF2LFjB3r37g09PT0ArwoyR48eRWpqqsJ97O3tERoaiq1bt+LYsWPlHqOy8TVl1KhRsLa2xty5czF//nwMGjQI3bt3V3VaRERERET0nmGBg2olScHA29sbBQUFyMnJgb6+PpydnREdHQ2xWCy3T69evWQeU9m1axfc3d1Rt27das83OTkZmZmZ+Pjjj5GTk4OcnBx4eXkBALZt21bqfpMmTcKHH35Y4UdPKhtfmjcXaq0KbW1tRERE4OHDh9DR0cH06dOV1jcREREREVFFscBBtdKhQ4eQlZWFuLg4ODo6Sn+OHTtW6toWvr6+yMzMxOnTpyEWi7Fv374aezwlNjYWADBs2DBprr169UJJSQliY2NRWFiocD99fX3MmzcPjx49qtCjJxWJNzQ0LPV4ku0GBgYVGVaFWVpawszMDI6OjqhTp45S+yYiIiIiIqoIvkWFaqWYmBg0a9YMERERcm3h4eGIioqSe/TEysoKLVu2xJ49e1BUVISCggK4u7tXe67Z2dk4fPgwAgMD5QoqFy9exMKFC3HgwAH4+voq3L9Tp04ICQnBpk2bYGtrW+7xyos3NTXF48ePFe4rWb/E1NS03OMQERERERGpExY4qNbJysrCkSNHMGzYMDg7O8u1+/r6IioqSuHaFr6+voiOjkZ+fj569OghXQ+jOiUkJKCoqAhhYWFo1aqVTJu9vT3WrVuH//3vf6UWOABgypQpOHToEH788ccKHbOseCcnJxw6dAi5ublyj+fs2bMHderUqVAhhYiIiIiISJ3wERWqdeLj41FcXFzq4yV+fn4oKSlRuLaF5G0qkgU/a0JcXBzatWsnV9wAAF1dXfj6+uLkyZO4detWqX1IHj2p6LoaZcWPGTMGhYWFGDx4MOLi4nD69GkkJSXh66+/xrZt2zB16tQaKfwQERERERHVJBY4qNaJj49H27ZtYWVlpbC9ffv2aNWqFWJjY1FUVCTT1qZNG4hEItStWxcuLi7VnuuFCxdw8+ZN9OnTp9QYyWtut27dWmZfDg4OCA4OrvCxS4tv3rw5YmNjYWNjg+XLlyMsLAwzZsxAamoq1q5di6CgoAofg4iIiIiISF1oCIIgqDoJInq/eHl54fbt28jvPgl6jduoOh0itVWQfhPpGyfhzJkz6Nixo6rTISIiIqo0ydsn//zzzyr3xTU4iEpRXFxcboyGhga0tLRqIBsiIiIiIiIqCwscRKWwtrYuN8bJyQmbN2+ugWyIiIiIiIioLCxwEJUiJiam3Jg6derUQCZERERERERUHhY4iErBV6kSERERERGpD75FhYiIiIiIiIjUHu/gICKVKcpOVXUKRGqNv0NERERE/2KBg4hUQlNTE9mJi1SdBpHa0zcwhKmpqarTICIiIlI5FjiISCXMzc0RHR2t6jSI1J6pqSksLCxUnQYRERGRyrHAQUQqoa2tjY4dO6o6DSIiIiIiekdwkVEiIiIiIiIiUnsscBARERERERGR2mOBg4iIiIiIiIjUHtfgICKVKC4uxtmzZ1WdBlGtx0VEiYiIiCqGBQ4iUom0tDR06tRJ1WkQ1Xr6Bob4v2tXWeQgIiIiKgcLHESkEiUlJWjQ5wvoNGiu6lSIaq2i7FRkJy5CVlYWCxxERERE5WCBg4hURqdBc+g1bqPqNIiIiIiI6B3ARUaJiIiIiIiISO2xwEFEREREREREao8FDiIiIiIiIiJSeyxwEBEREREREZHaY4GDiIiIiIiIiNQeCxxEREREREREpPYq9ZrYkJAQnDx5UmabhoYG6tSpg1atWiEsLAy9e/cGAEyfPh3x8fGl9mVsbIwTJ07IbEtPT8fvv/+OQ4cO4cGDBwCADz/8ED179kRISAgMDQ2lsStWrMDKlSvxf//3fzJ9vHjxAhs3bsSePXtw//596OrqolWrVvDz88PAgQOhrf3vkOPi4jBjxgwMHToUs2bNksuxtGOUx9LSUm6bnp4emjdvjgEDBmD48OHQ1JStLR08eBBbtmzBpUuXkJeXhyZNmsDd3R1hYWFo0qTJW/fv6ekJJycnzJ8/v9RcJ0yYgM8//7xC8W9atmwZVq9ejaCgIHz//fcybdHR0Zg9e3a5fSQnJ8PU1BTW1taYOHEixo0bJ9N++vRp/Pbbbzh37hz++ecfmJmZwdXVFaGhoWjdurVMbLdu3fDy5UskJiaicePGMm3FxcWlHqMsS5Yswdq1a+W2GxgYoGnTpvD19cXYsWOhpaVV4XHv3r0brVu3lsYmJyfL5avIP//8g65du0IsFiMpKQkNGzaUaXd1dUVWVlaZfQQGBuKHH37AwoULsXHjRly6dAl//fUXwsLCMHv2bISEhCjcb9euXZgyZQo2btwIe3t7tG/fvszjjBkzBpMnTy53TERERERERMpQqQIHALRr1w7ffvut9LNYLEZ6ejo2bNiAKVOmoF69eujWrRsAoGHDhli5cqXiA2vLHvrEiRMIDw+HkZERhgwZAktLS5SUlODEiRNYu3Yt9u7diy1btkBfX7/U3B49eoTPPvsMT58+RUhICDp16oSCggIcO3YMc+fORWJiIlavXo169erJ7Ld582b4+PjAwcGhsqejVAEBAfjkk0+kn/Py8rBv3z4sXLgQOTk5+OKLL6Rt33//PbZs2QJfX1/85z//gZGREW7evInNmzcjPj4ey5cvR+fOnd+6/+pSUlKChIQEiEQi7Ny5E1OnTkWdOnWk7V5eXmjbtq3088GDB/Hf//4Xa9asgYmJiXT76//3m9asWYOlS5fCzc0Ns2bNgqmpKe7fv4/IyEj4+/tj/vz56NWrl8w+//zzD77++musW7dOaWPV0tLCli1bZLY9efIEO3fuxIoVK1BcXIxJkybJ5V7a2MzNzd8qj8TERNSrVw9isRgxMTEYO3asTPvPP/+MoqIi6efRo0fD0dERI0aMkG5r0KCBXL+dO3eGubk5du7cWWqBIyEhARYWFnB2dkZhYSEA4NNPP0X//v0VxlekYENERERERKQslS5w1K1bF3Z2dnLbu3fvji5duiA2NlZa4NDV1VUY+6YnT55g8uTJaN68OTZt2iRzp4arqyu8vb0RFBSEjRs3YvTo0Qr7EAQB4eHhyMvLQ3x8PJo2bSptc3d3R69evTB06FD88MMPWLBggcy+9erVw8yZM7Fjx44yCyiV0bhxY7mxd+nSBbdv30ZkZCTCw8Oho6ODyMhIbNmyBXPmzJEpWHTu3BkDBgzAyJEjMWnSJCQmJsLU1LTS/VenY8eO4eHDh4iMjMTQoUOxc+dOBAUFSdtNTExkLvBv3LgB4FWRTNHdFW86cOAAli5dis8//xwTJkyQbnd2dkb//v0xefJkTJ8+HSKRSOZODiMjIxw+fBixsbEYOHCg0saraC57enri/v37iImJkStwKBpnVcXGxsLNzQ06OjqIjo7G6NGjZe4Gsra2lonX1taGiYlJub+HGhoa8PPzw4oVK3Dv3j20aNFCpj0zMxNHjx5FeHg4NDQ0pNubNGlSod9xIiIiIiKi6qa0NTh0dXXf+oJ6y5YtyM7ORkREhExxQ6J9+/YIDQ2VuTvgTcnJybh48SK+/PJLmeKGhL29PUJDQ7Fjxw7cv39fpm3atGm4f/8+Fi9e/Fb5V4aNjQ1evHiB58+fQywWY82aNXB1dZUpbkjUrVsXc+bMwdOnTxEZGVnp/qtbbGwsWrVqBQcHB3Tp0gVRUVFK7X/lypVo06aNTHFDQldXF//5z38gCALWr18v09ajRw84ODhg/vz5yMjIUGpOirx5R1B1uXHjBi5dugR3d3f069cPDx48wOHDh5XW/8CBA6GpqYmdO3fKte3YsQMA4Ofnp7TjERERERERKVOlCxyCIKC4uFj6U1BQgHv37mH27Nl48eKF3O3qr8e+/iMIgjTmzz//hKWlpczjDG+aNm0agoODS20/cuQINDU14eHhUWqMZH2QgwcPymzv3LkzAgMDsXnzZpw+fbrM8VfVnTt3YGhoiAYNGuDq1avIzMyEl5dXqfGtW7eGlZUV/vzzz0r3X52eP3+OAwcOSC94/f39cfXqVVy4cEEp/WdlZeHq1avw9PQsNcbExARdunSR+z41NDQwb948FBYW4uuvv1ZKPoDsXC4sLERGRgbWr1+P48ePY8CAAXLxYrFY4dwvKSl5q+PHxMTA2NgYHh4ecHBwQIsWLZRaVGrSpAlcXFwUFjgSEhLQrVs3NGrUSGZ7SUlJqb/jRERERERENanSj6icOnVK7jZ4DQ0NiEQiLFu2TOaC9MGDB3KxEq8v9Hj//n24urrKxSi6SHpz7Q6JtLQ0GBsbo27duqXmbmFhIY1909SpU3HkyBHMmjUL27dvr/KjKpILP+BVUSgrKws7d+7EwYMHMXz4cGhoaEjzKG89hhYtWuDo0aOV7r867dy5E2KxWFrQ6tGjB+rXr4+oqCh06NChyv1X9NxYWFjg0KFDyM3NlfnuW7RogcmTJyMiIgJxcXHw9/evUj5isVjhXG7atCkmTZoks8aFRGnFma5du+KXX36p1PGLioqwY8cO9OnTB7q6ugBeFZWWLVuGhw8fKrxr6W0MHDgQkydPxsWLF6WLiF65cgXXr1/HxIkT5eKXLFmCJUuWKOzrr7/+KnN9FSIiIiIiImWqdIHD2tpa+raMjIwMLFu2DEVFRViyZIncGy0aNmyINWvWKOzn9b8EK/qLtuSNF28q7Y0mgiCUWvyQKKu9Tp06mDt3LsLCwrBkyRLMmDGjzL7Ks3r1aqxevVpmm56eHgIDAxEeHi7Nuby8gFcLXL5+x0tF+6+otymGxMbGwtHREQYGBsjJyQEAeHt7Y9euXZgxYwaMjIwq3efrKnpuymofOnQo9u7di4iICLi6ulbprhYtLS1s3boVAJCbm4uNGzfizJkz+Prrr0stZPz8888KL/Df5pGWpKQkPHnyBN7e3jLne+nSpdi2bZvc+h9vy9vbG8bGxtixY4e0wBEfH4+GDRvC3d1dLj44OFjh3SsAqjwHiIiIiIiIKqPSBY46derA1tYWAGBrawt7e3v0798fw4YNQ3x8vMwFna6urjS2LObm5tLXwkoT09ZGTEyM9PO2bduwbdu2Uvto1qwZjh49ivz8/FLvvkhNTQWAUv/a3aVLFwQGBmLTpk3w8fEpN++yDBo0CIMGDQLw76t0zc3NZdYpadasGQDIjV1R3pLYyvQPAIaGhtI3XrxJst3AwKASIwOuXbuGK1euAAAcHR3l2uPj4xEaGlqpPt9UmXNjZGSk8M4dTU1NzJs3DwMGDMC3335b6ht9Kur1uezk5ITPPvsM4eHh2LRpEzp27CgXb2lpqbRFRmNjYwEAYWFhcm0xMTGYMGFCucWgitDV1UW/fv2wa9cuTJ8+HcCr18P6+/sr7L9Ro0YV+h0nIiIiIiKqblVeZLRBgwb45ptvkJ6ejrlz575VH56enrh8+bK0ACFha2sr/TEzM/JnMRcAACAASURBVCu3j+LiYhw4cKDUmD179khjSzN16lQ0btwYM2bMQH5+fiVGIcvMzEyau42NDVq2bClXfLCxsYGZmRn27t1baj+pqam4cuWKXM4V6R8ATE1N8fjxY4V9p6enS2MqIyYmBgYGBtiwYQM2bdok89O6dWvpnQ5VYWZmBhsbG+zfv1/u7hWJnJwcHDt2rMw1TFq2bImJEyciKSkJCQkJVc5LQktLC/PmzYO2tjZmzJhRahFJGR4/fowjR47g008/lTvfU6ZMQWZmZoXXaKmIgIAAZGdn49ixYzhy5Aiys7MREBCgtP6JiIiIiIiqg1LeovLxxx/Dzc0NiYmJOHHiRKX3Dw4OhomJCaZNm4bc3Fy5drFYjNu3b5fZh6urKzp16oQff/xR4V/9L126hPXr18PX1xcffvhhqf1I3lxy9+5dpVyol0VTUxMTJkxASkqKwrtT8vPzMXPmTNSrVw+ffvrpWx3DyckJFy9exMOHD+Xa9uzZAy0tLYV3YZSmsLAQO3fuhKenJ7p06QJnZ2eZH39/f9y6dQsnT558q3xfFx4ejuvXr2PFihVybWKxGN988w2Ki4sxbNiwMvsJCwuDvb095s+fX+WcXmdubo7Ro0fj7t27cm9yUaaEhASIxWKEhYXJne+wsDDUq1dPqYuNWlpawsbGBvv27cMff/wBJyenMn9niIiIiIiIaoOq39P+/82cORP9+vXDnDlzEB8fD+DVxfD58+dL3UckEsHQ0BANGzbEihUrMHHiRPTt2xeBgYGwsbGBpqYmLl++jJiYGNy9exd9+/YttS9NTU0sWrQIo0aNgr+/P0JDQ2Fvb4+SkhIcO3YMkZGRaNeunXT9kLJIXtsaHR1d+RNRSYGBgbh16xa++eYbnDhxAr169UL9+vVx+/ZtbNy4EZmZmVi6dKnc2ysqKjg4GNHR0QgODsaIESPQtm1b5Ofn49ixY9i8eTNGjBgh98jOzZs3sWHDBrm+7Ozs8PDhQzx79kz6Rpo39evXD4sXL0ZUVBScnJzeKmeJ7t2748svv8TixYtx+fJl9O/fH2ZmZkhNTcWWLVtw/fp1zJs3DyKRqMx+Xn9URdmGDx+O2NhY/PzzzxgwYIDMubxy5Yr0Lpk3NWvWDA0bNpR+jomJkXvMRlNTE0OHDkVcXBxsbW3RokULuX709PTQq1cvREdH4969ewpj3kZAQACWL1+O4uJizJo1q9S4R48elfo7bmhoWO53Q0REREREpCxKK3C0atUKISEh+PXXX/H7778DADIzMxEYGFjqPjExMdLn9zt16oQdO3bgf//7H/bu3Yv169ejsLAQTZo0QefOnbFkyRK0a9euzByaNGmCrVu34n//+x8SExOxfv16aGlpoXXr1pg+fToCAgIqvE7B9OnTkZKSgkePHlXwDLy9mTNnws3NDZGRkfjuu++Qk5ODJk2awN3dHaGhoVV6Q4aRkRFiYmKwevVq/Prrr3j8+DH09fXRqlUrzJkzR+FF/6VLl3Dp0iW57RMmTMCFCxdQv359uLm5KTyemZkZXFxcsG/fPmRnZ1f5dbUjR46Eg4MDNm7ciB9//BFPnjyBmZkZXF1dMX/+fLmFbUvTqlUrhIeHY8GCBVXK5026urqYPn06xo8fj/nz52P58uXStrFjx5a634wZM2TW01B0l4qWlhasra1x586dMhe9HTBgALZt24aoqChMmzbt7Qbyhj59+mD+/PnQ0dFBz549S43bsmULtmzZorDN2toacXFxSsmHiIiIiIioPBpCaQscEBFVEy8vL9y+fRv53SdBr3EbVadDVGsVpN9E+sZJOHPmjMLFjImIiIjUnWRNRWWsK6i0OzjedWKxuNTFLiU0NDSgpaVVQxlRVQiCALFYXG6clpbWW71Gl4iIiIiIiGoWCxwV1KNHj3JfWdqsWTMcPHiwhjKiqoiJicHs2bPLjfvpp5/Qv3//GsiIiIiIiIiIqoIFjgpas2ZNua8C1dXVraFsqKq8vLxgZWVVblzz5s1rIBsiIiIiIiKqKhY4KsjS0lLVKZASmZiYwMTERNVpEBERERERkZJoqjoBIiIiIiIiIqKq4h0cRKQyRdmpqk6BqFbj7wgRERFRxbHAQUQqoampiezERapOg6jW0zcwhKmpqarTICIiIqr1WOAgIpUwNzdHdHS0qtMgqvVMTU1hYWGh6jSIiIiIaj0WOIhIJbS1tdGxY0dVp0FERERERO8ILjJKRERERERERGqPBQ4iIiIiIiIiUnsscBARERERERGR2mOBg4iIiIiIiIjUHgscRERERERERKT2WOAgIiIiIiIiIrXHAgcRERERERERqT0WOIiIiIiIiIhI7bHAQURERERERERqjwUOIiIiIiIiIlJ7LHAQERERERERkdpjgYOIiIiIiIiI1B4LHERERERERESk9ljgICIiIiIiIiK1xwIHEREREREREak9FjiIiIiIiIiISO1pqzoBInr/PH78GGKxGF5eXqpOhYiIiIiIVOjRo0fQ0tJSSl+8g4OIapyenh60tVlfJSIiIiJ632lra0NPT08pfWkIgiAopSciIiIiIiIiIhXhHRxEREREREREpPZY4CAiIiIiIiIitccCBxERERERERGpPRY4iIiIiIiIiEjtscBBREqRnZ2NcePGwcHBAc7Ozpg7dy6Ki4sVxiYnJ6Nv376ws7NDr169kJSUJNO+bt06dOvWDXZ2dggJCcHt27drYgikBpQ1zwoKCjB37lx069YNnTp1wieffILjx4/X1DCollPmv2cS0dHRsLS0rM60Sc0oc55t2bIFPXr0gL29Pfr27VvqPKT3j7LmWX5+Pr755hu4urrC0dERoaGhuHbtWk0Ng2q5yswzib1798LLy0tue5WvAwQiIiUIDg4WvvjiC+Hly5fC/fv3hd69ewvr1q2Ti7tz545ga2sr7N+/XygqKhJ27doltG/fXkhPTxcEQRDi4uIENzc34fr160J+fr4QEREh9O7dWygpKanpIVEtpKx5NmfOHMHf3194+PChUFxcLGzdulXo0KGD8ODBg5oeEtVCyppnEtevXxfs7OwEkUhUU0MgNaDM/7/p4uIiXLhwQSgpKRF27twpWFtby81Dej8pa5799NNPQkhIiPD06VOhoKBAmDdvnuDl5VXTw6FaqqLzTBAEobCwUPj555+Fdu3aCR4eHjJtyrgO4B0cRFRl9+7dw8mTJ/HVV1/BwMAAzZs3x7hx4xAZGSkXGx8fDwcHB3h7e0NbWxu+vr5wdHTE1q1bAQDbtm3Dp59+irZt20JPTw9ffPEFHj58iBMnTtT0sKiWUeY8KygoQHh4OJo0aQItLS0MGjQIurq6uHz5ck0Pi2oZZc4zAMjLy8OUKVMwdOjQmhwG1XLKnGe//vorJk6ciPbt20NDQwN9+vTB1q1bUbdu3ZoeFtUyypxnt27dgiAIEAQBAKCpqQkDA4MaHQ/VTpWZZwAwbNgwnDhxAiNHjpRrU8Z1AAscRFRlN27cgLGxMRo1aiTd1rp1azx8+BA5OTkysTdv3oRIJJLZ1qZNG+ltjm+26+jo4MMPP+RtkKTUefbDDz+ge/fu0ra//voL//zzD6ysrKpxBKQOlDnPgFdzzd3dHS4uLtWbOKkVZc2zvLw83LhxA5qamhgyZAicnZ0RFBSEvLw81KlTp0bGQrWXMv89GzZsGK5fv47OnTvDzs4OO3bswNKlS6t/EFTrVWaeAcCCBQuwfv16WFhYyLUp4zqABQ4iqrIXL17IVfEln1++fFlurL6+vjSuvHZ6fylznr3u/PnzmDRpEiZMmIDmzZsrOWtSN8qcZ9u3b8etW7cwceLEasyY1JGy5llOTg4EQcCvv/6K7777DkeOHEGfPn0wcuRIpKWlVe8gqNZT5r9nYrEYPj4+OHz4ME6ePAkvLy+MGzcOBQUF1TgCUgeVmWcA0Lhx40r1VdnrABY4iKjKDA0NkZeXJ7NN8vnNvyAZGBggPz9fZlt+fr40rrx2en8pc55JREdH47PPPsOYMWMwfvz4asia1I2y5tnt27exaNEiLFq0CNra2tWbNKkdZc0zHR0dAMBnn32Gtm3bQldXF8HBwWjatCmSk5OrcQSkDpQ1z4qKijBx4kT4+/ujUaNGqFu3Lr7++mtkZGTg6NGj1TsIqvUqM8/Ko4zrABY4iKjK2rZti2fPniErK0u67datW2jcuDHq1asnEysSiXDjxg2ZbTdv3kTbtm2lfb3eXlRUhLt378rdNknvH2XOM7FYjG+++QaLFi3CqlWr8Nlnn1X/AEgtKGue7d27Fzk5OfDz84ODgwPGjBkDAHBwcMDOnTurfyBUqylrnpmYmKBBgwYoLCyUaReLxdWXPKkNZc2zly9f4vnz5zLzTEtLCxoaGtIiG72/KjPPKtJXVa8DWOAgoir78MMP0alTJ8ybNw+5ublITU3F6tWrERAQIBfbr18/nDx5Ert370ZxcTF2796NkydPon///gCAgQMH4vfff8e1a9dQUFCARYsWwdTUFA4ODjU9LKpllDnPIiIicPjwYcTGxnJtBJKhrHk2duxYnD9/HqdPn8bp06exdu1aAMDp06fRt2/fmh4W1TLK/PcsKCgIq1atwtWrV1FcXIxNmzYhIyMD3t7eNT0sqmWUNc/q16+PTp06YeHChcjOzkZBQQEWLFiADz74AJ06dVLByKg2qcw8K49SrgPe/mUwRET/yszMFD7//HPByclJ6Ny5szB//nyhuLhYEARBsLOzE7Zv3y6NPXz4sNCvXz/Bzs5O6N27t3Do0CFpW0lJifDLL78Inp6egp2dnRASEiLcvn27xsdDtZMy5ll2drZgZWUlWFtbC3Z2djI/r+9P7y9l/Xv2uuPHj/M1sSRDWfNMLBYLv/zyi/Dxxx8LdnZ2gr+/v3Dq1KkaHw/VTsqaZ5mZmcJXX30luLi4CE5OTsLIkSP5v89IqjLzTCI2NlbuNbHKuA7QEIT//64fIiIiIiIiIiI1xUdUiIiIiIiIiEjtscBBRERERERERGqPBQ4iIiIiIiIiUnsscBARERERERGR2mOBg4iIiIiIiIjUHgscRERERERERKT2WOAgIiIiIiIiIrXHAgcRERERkQoIgqDqFIiI3ikscBAREZHaOXHiBCwtLWFlZYVTp06VGRsSEgJLS0scO3ashrKrWdOnT4elpSWio6Ol21asWAFLS0ssWbJEhZn9q7LfgaenJywtLXHixIlqzkx1UlJSMGzYMFWnQUT0TmGBg4iIiNSWIAiYOXMm8vLyVJ0KUYU9fPgQw4cPx61bt1SdChHRO4UFDiIiIlJr9+/fx6JFi1SdRq0yZMgQ7N69G6GhoapOhRQoKSlRdQpERO8kFjiIiIhIbTVs2BDa2tqIjIzE6dOnVZ1OrWFiYoLWrVvDxMRE1akQERHVGBY4iIiISG01b94cI0aMQElJCWbOnIn8/PxK7X/o0CEMHz4cTk5OsLW1hY+PDxYsWIBnz57JxVpaWqJ///44efIkevbsKY1PTU2VrnmRlJSEAwcOIDAwEHZ2dnB2dsYXX3yBrKwsAEBMTAz69u2LDh06wMfHBytXrkRRUZHcsf7++2989dVX8PT0hK2tLezs7NCrVy8sWLAAOTk55Y7rzTU40tLSYGlpWe7Pm2teXLx4EeHh4ejSpQtsbGzg5eWFiIgIPHnyROFx//77b4wfPx5dunSBvb09RowYgWvXrpWbb0VI1l358ccfcf36dYwdOxaOjo6wt7dHSEgILl68CAA4ffo0hg4dCnt7e7i5uWHatGnIzs6W6UuybsmlS5cQGRkJHx8ftG/fHt7e3li8eDFyc3MV5nD27FmMHz8enTt3ho2NDTw8PPDtt9/i0aNHcrGenp5wcHDA9evX4e/vL40fNmwYvLy8AAAZGRmwtLSEp6enzL579uzBqFGj4OrqChsbG3Tq1AmDBg1CZGSk3N0fkvVNnj17ho0bN6JPnz6wtbWFi4sLZsyYgYcPHyocy/HjxzFu3Di4urrC3t4effv2xX//+1+Fj3vduXMH06ZNg5ubG2xsbNCtWzfMmjULDx48KOXbIiJSDRY4iIiISK2NHz8ebdu2xb1797B48eIK77dw4UKMHj0af/31FywtLeHh4YG8vDysX78e/v7+SE1NldsnOzsbY8eOhba2Nrp27Qo9PT2Ym5tL26OiojB+/HgUFBTAxcUFGhoaSExMxKhRo7Bw4UJ8/fXXqFevHjp37owHDx5gxYoV+PHHH2WO8ccff2DQoEFITExEo0aN4OHhAWtra9y/fx/r16/HZ599VulHHAwNDdG3b1+FP+3bt5fGNG3aVLpPfHw8goKCsG/fPjRp0gSenp7Q1NTEhg0bEBAQgLS0NJljJCcnY/DgwThw4AAsLCzg5uaGq1evYvDgwXKxVXH58mUMGjQIV69ehbOzM8zMzHDy5EmEhoZi27ZtGDp0KLKzs+Hq6orCwkIkJCRg2LBhCt9YsmrVKvzwww/Q09ODu7s78vLy8N///hdDhgzB8+fPZWIjIyMxZMgQ6fg8PT2ho6ODqKgoDBgwQFpgeV1RURFGjRqF58+fo3v37tDQ0MCAAQPg7e0NADAwMEDfvn2lnwFgzpw5mDhxIk6dOgUrKyt4enrC3NwcFy5cwA8//CA3XyRmz56NefPmwdDQEN27d4dYLEZcXBwGDx6Mf/75RyZ23bp1CAsLQ1JSEj788EO4urri6dOnWLx4MUaOHInCwkJpbEpKCvz8/JCQkABjY2N4eHigfv36iImJgb+/P/7++++Kf3lERNVNICIiIlIzx48fF0QikRAUFCQIgiBcuHBB+OijjwQrKyvh9OnTMrHBwcGCSCQSjh49Kt32559/CiKRSHBychIuXrwo3V5QUCDMmjVLEIlEgp+fn1BSUiJtE4lEgkgkEkaPHi2IxWJBEATpf5cvXy5t37x5s3Sf9PR0oUOHDoJIJBKsrKyElJQUaVtKSoogEokEOzs7obi4WHp8Z2dnwdraWjh79qzMOG7evCl07NhREIlEMmOcNm2aIBKJhG3btkm3SfJZvHhxmecxMzNTcHd3F0QikfDHH3/IHMva2lqws7MTjh07Jt0uFouFxYsXy5x7QRCE3NxcwdXVVRCJREJCQoJ0+4sXL4Thw4dLz83r30FZPDw8BJFIJBw/fly6TfKdi0QiYfLkyUJhYaH0nPn7+0vbli5dKjM+R0dHQSQSCefOnZM7ZyKRSPj5559lxhEWFiaIRCLh+++/l26/cuWKYGVlJdja2gqHDh2SOR8rVqwQRCKR0K1bNyEvL09uDH5+fkJ+fr40XhAEITU1VRCJRIKbm5vMuC9duiSIRCLB29tbyM7OlmnbuXOnIBKJhA4dOkjHLgj/zu8OHTrIfFdPnz4VevToITcnL168KFhZWQkdO3aUmUcvXryQ9vXbb78JgiAI2dnZgqOjo/DRRx8JiYmJMvlERUUJIpFI8PLyEgoKCgQiotqAd3AQERGR2mvfvj2GDRtW4UdVNmzYAACYOnUqbG1tpdt1dXXx3XffoUWLFrh8+TKOHz8ut+/QoUOhqfnqf0JJ/ishEokQHBws/dyoUSM4OjoCAHr27AlXV1dpm6urK+rWrYuXL18iMzMTAJCVlYWuXbti2LBhsLe3l+m7devW6Ny5MwAo5Y6IwsJCTJgwAQ8fPsSYMWPQs2dPadumTZtQVFSECRMmoEuXLtLtmpqamDRpEkQiEc6ePYvz588DAA4cOIDMzEx4eXmhf//+0nhDQ0P8+OOP0NHRqXK+EhoaGpg9e7a0T11dXWnujRo1wvjx46Wxpqam6NSpEwDg3r17cn25urpi5MiR0s916tTB/Pnzoa2tjbi4OBQUFAB4dT5KSkowYsQIdO/eXRqvqamJCRMmwMnJCenp6di5c6fcMQYPHgw9PT1pfFlycnLg4+ODSZMmya2f0qdPHxgZGSEvL0/ukRsAGDRokMx3ZWxsjH79+gEArl+/Lt2+detWlJSUYMyYMdJzA7z6rr766itYWFjg8ePHAF49UvX8+XMEBQWhd+/eMscLDAyEu7s7UlNTsX///jLHRURUU1jgICIiondCeHg4Wrdujbt372Lp0qWlxhUXF+Ps2bPQ0NCAj4+PXLu2tjY+/vhjAJBbkwJ4VcQoTYcOHeS2SS5UP/roI7k2IyMjAJBeSDdt2hQLFy7ElClTpDGCIODhw4fYv3+/tLDx+iMEb2vWrFk4d+4cPDw8MHHiRJk2SWHn9QtmCQ0NDbi5uQEATp48CQA4deoUAMhc/Es0aNAAHTt2rHK+EhYWFnIX/5LPbdu2hba2tkyb5BwrOmdvXrQDr4ok7du3R15eHi5cuADg3/H16tVLYU6+vr4A/j0frytrvrzJxcUFy5cvl8mruLgYN2/eRExMjPTRJEVjsbOzUzgWADIFP0mOb677AbwqFO7fvx9Tp04F8O/8VzQPAKBbt24ycUREqqZdfggRERFR7aerq4uIiAgMHjwYGzduhI+Pj9xdEADw7NkzFBUV4YMPPkDdunUV9iVZV0NyZ8XrJBfMitSvX19um4aGBgDggw8+KLXtTcnJyUhISMCNGzeQmpoqvUAtLb6y1qxZgx07dqBVq1ZYuHCh3J0F6enpAAA/P78y+5Esrin5i3/jxo0VxpmbmyvtIlhZ5xgAWrRooXB7kyZNAPw7Lsl/mzVrpjD+beeLIgUFBUhISMCff/6J27dv49GjRyguLgbw71gEBeuJKDovWlpaAGRfSyvJ8fX1Vkoj+X4nTJhQZpxkvhARqRoLHERERPTO6NChA8LCwvDLL79gxowZ2L59u1yM5OKwrAtfSYyurq5cW1mPGbx590BllZSUYNy4cUhKSoKOjg5sbGzQr18/tG3bFnZ2dvj9998Vjqky9u3bh2XLlqFevXpYvXq1wiKPWCwG8OoOh7LGa2VlBaD8wktVz0t19SUpALxJ8v1L2subM287X970+PFjBAcH4969ezAyMoKtrS08PDxgaWkJJycnhIaGlvpWlIoWvyTFkoqQzAMPD49Si4EA0KZNmwr3SURUnVjgICIionfKxIkTcfDgQdy5c0fhoyrGxsbQ0dHBs2fPkJubq/DCTfIGlQYNGlR7vq/bvn07kpKSYGVlhXXr1sHMzEym/c23YVTWlStXMHXqVGhoaGDRokVo2bKlwjgzMzM8ePAAEydOLPUuh9dJHoUobW0QyR0QtU1GRobC7ZIiguRODjMzM6SlpSEtLQ1t27aVi1fWfFmyZAnu3buHfv36Ye7cuXIFk4q8Irg8DRs2xIMHD5Cenq7w+4+KipK+vcfMzAx3797F0KFD4eLiUuVjExFVN67BQURERO8UPT09RERESF9pevv2bZl2HR0d2Nvbo6SkROHiiMXFxdLtzs7ONZKzxLlz5wAA/v7+csWNFy9eSNsr+5pY4FWRYcyYMcjLy8PkyZMVrpchIVkYNTk5WWH71KlTMWjQIBw8eBAApBe/Bw4ckIvNzc2VrmFR2ygaX3p6Oi5dugRjY2PpArSS87F3716F/fzxxx8AKj5fSrvbQvL9Dh8+XK64ceHCBeTm5gJ4u+9fQrKw6OHDh+Xabt26hW+//RYrVqwAUP48WLRoEfz9/REdHf3W+RARKRMLHERERPTOsbe3R2hoKEpKSpCVlSXXHhoaCgD46aefcOXKFen2oqIifP/997h//z4++ugjmbdM1ATJYpmHDx+WeZTg6dOnmDx5Mp4+fQrg30VJKyo/Px/jxo1DRkYG+vTpg1GjRpUZHxISAi0tLSxbtgx//fWXTFtUVBS2b9+Oq1evon379gBeLVjZokULHDt2TPqGGuDVYpjffPON9MK8tklISJApcuXm5mLq1KkQi8XScwAAwcHB0NLSwrp165CSkiKNFwQBK1euxKlTp9CoUSN4e3tX6LiSt6q8fPlSplgh+f7fLBRdv34dX331lfRzZb//1w0ZMgQaGhpYvXo1rl27Jt3+4sUL/PDDDwAgfftKYGAgDA0N8fvvv2PXrl0y/SQlJeG3337D5cuXYWNj89b5EBEpEx9RISIionfSpEmTkJSUhLt378q1eXt7Y9iwYfj1118REBCATp064YMPPsCFCxeQnp6OZs2aYcmSJZVaP0EZAgICsHnzZqSkpODjjz+GtbU1cnNzcfbsWeTn56NNmza4efOmwqJNWZYvX45Lly5BS0sLderUwaxZs1BYWCi3WKWjoyMCAwNhY2ODmTNnYs6cOQgLC0O7du1gbm6OO3fu4MaNG9DS0sKCBQtgamoK4NXaEwsWLMCIESMQERGBhIQEWFhY4OLFi8jOzoa1tTUuX76stPOkLGZmZpgwYQI6duyIhg0b4tSpU3jy5AlcXFxkikA2NjaYMWMG5s6di+HDh8POzg6NGzfGtWvXcPfuXRgbG2PZsmVlrlPxOhMTExgZGSEnJwdBQUGwsLDAwoULERYWhjNnzmDFihU4ePAgzM3NkZGRgQsXLkBPTw/m5uZIS0ur9Pf/Ojs7O0yZMgWLFi3CwIED4ejoCAMDA1y4cAHZ2dno1q0bhg4dCuDVo0c//vgjpkyZgilTpmDVqlVo1aoVHj16hL///hsAMGPGDIVvCCIiUgXewUFERETvJH19fcybN6/UIsW0adOwevVqODs749q1azh06BDq1KmDsWPHIj4+vtT1KaqTubk5oqOj0bNnT4jFYiQnJyM1NRVdunTBr7/+ip9++gnAq7+eV8aTJ08AvFo0cuvWrYiJicGOHTuwc+dOmR/JIxLAq7sWIiMj0aNHD6SnpyMpKQkvX75Er169EBMTg549e8oco0OHDti2bRv69euHrKwsJCcno2nTpvjtt99q7QXwuHHj8OWXX+Lx48c4dOgQTE1NMWPGDKxbt07uEZGQkBD8/vvv8PT0xN27d3Hw4EGUlJQgNDQUO3bsUPjGntJoampi4cKFaN26Na5chXkPzwAAARdJREFUuYKjR4/i+fPn+Pjjj/HLL7/A0dERDx48QEpKCnJzc+Hn54f4+HgEBwcDqPz3/6ZRo0Zh/fr1cHZ2xuXLl3HkyBHUr18fkydPxqpVq2R+Zz7++GPExsaiX79++Oeff3Do0CFkZWXB3d0dmzZtQlhYWJVyISJSJg1B0XumiIiIiIjeUdOnT0d8fDzmzJmDTz75RNXpEBGRkvAODiIiIiIiIiJSeyxwEBEREREREZHaY4GDiIiIiIiIiNQe1+AgIiIiIiIiIrXHOziIiIiIiIiISO2xwEFERERERET/rx07IAEAAAAQ9P91OwKdIewJDgAAAGBPcAAAAAB7ggMAAADYExwAAADAnuAAAAAA9gQHAAAAsBfgIMIwDiEASgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "120 features required for 0.99 of cumulative importance\n"
     ]
    }
   ],
   "source": [
    "fs.plot_feature_importances(threshold = 0.99, plot_n = 12)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "All of the feature importances are accessible in the `feature_importances` attribute of the `FeatureSelector`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>feature</th>\n",
       "      <th>importance</th>\n",
       "      <th>normalized_importance</th>\n",
       "      <th>cumulative_importance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>EXT_SOURCE_2</td>\n",
       "      <td>270.6</td>\n",
       "      <td>0.095754</td>\n",
       "      <td>0.095754</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>EXT_SOURCE_3</td>\n",
       "      <td>250.8</td>\n",
       "      <td>0.088747</td>\n",
       "      <td>0.184501</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>EXT_SOURCE_1</td>\n",
       "      <td>161.9</td>\n",
       "      <td>0.057289</td>\n",
       "      <td>0.241791</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>DAYS_BIRTH</td>\n",
       "      <td>132.3</td>\n",
       "      <td>0.046815</td>\n",
       "      <td>0.288606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>SK_ID_CURR</td>\n",
       "      <td>121.7</td>\n",
       "      <td>0.043064</td>\n",
       "      <td>0.331670</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>DAYS_REGISTRATION</td>\n",
       "      <td>120.3</td>\n",
       "      <td>0.042569</td>\n",
       "      <td>0.374239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>DAYS_LAST_PHONE_CHANGE</td>\n",
       "      <td>110.9</td>\n",
       "      <td>0.039243</td>\n",
       "      <td>0.413482</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>DAYS_ID_PUBLISH</td>\n",
       "      <td>107.8</td>\n",
       "      <td>0.038146</td>\n",
       "      <td>0.451628</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>DAYS_EMPLOYED</td>\n",
       "      <td>104.4</td>\n",
       "      <td>0.036943</td>\n",
       "      <td>0.488570</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>AMT_INCOME_TOTAL</td>\n",
       "      <td>79.3</td>\n",
       "      <td>0.028061</td>\n",
       "      <td>0.516631</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  feature  importance  normalized_importance  \\\n",
       "0            EXT_SOURCE_2       270.6               0.095754   \n",
       "1            EXT_SOURCE_3       250.8               0.088747   \n",
       "2            EXT_SOURCE_1       161.9               0.057289   \n",
       "3              DAYS_BIRTH       132.3               0.046815   \n",
       "4              SK_ID_CURR       121.7               0.043064   \n",
       "5       DAYS_REGISTRATION       120.3               0.042569   \n",
       "6  DAYS_LAST_PHONE_CHANGE       110.9               0.039243   \n",
       "7         DAYS_ID_PUBLISH       107.8               0.038146   \n",
       "8           DAYS_EMPLOYED       104.4               0.036943   \n",
       "9        AMT_INCOME_TOTAL        79.3               0.028061   \n",
       "\n",
       "   cumulative_importance  \n",
       "0               0.095754  \n",
       "1               0.184501  \n",
       "2               0.241791  \n",
       "3               0.288606  \n",
       "4               0.331670  \n",
       "5               0.374239  \n",
       "6               0.413482  \n",
       "7               0.451628  \n",
       "8               0.488570  \n",
       "9               0.516631  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fs.feature_importances.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We could use these results to select only the 'n' most important features. For example, if we want the top 100 most importance, we could do the following."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "100"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "one_hundred_features = list(fs.feature_importances.loc[:99, 'feature'])\n",
    "len(one_hundred_features)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 5. Low Importance Features\n",
    "\n",
    "This method builds off the feature importances from the gradient boosting machine (`identify_zero_importance` must be run first) by finding the lowest importance features not needed to reach a specified cumulative total feature importance. For example, if we pass in 0.99, this will find the lowest important features that are not needed to reach 99% of the total feature importance. \n",
    "\n",
    "When using this method, we must have already run `identify_zero_importance` and need to pass in a `cumulative_importance` that accounts for that fraction of total feature importance.\n",
    "\n",
    "__Note of caution__: this method builds on the gradient boosting model features importances and again is non-deterministic. I advise running these two methods several times with varying parameters and testing each resulting set of features rather than picking one number and sticking to it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "119 features required for cumulative importance of 0.99 after one hot encoding.\n",
      "120 features do not contribute to cumulative importance of 0.99.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "fs.identify_low_importance(cumulative_importance = 0.99)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The low importance features to remove are those that do not contribute to the specified cumulative importance. These are also available in the `ops` dictionary. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['REG_REGION_NOT_WORK_REGION',\n",
       " 'FLAG_EMAIL',\n",
       " 'FLAG_DOCUMENT_6',\n",
       " 'NAME_FAMILY_STATUS_Separated',\n",
       " 'WALLSMATERIAL_MODE_Panel']"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "low_importance_features = fs.ops['low_importance']\n",
    "low_importance_features[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Removing Features\n",
    "\n",
    "Once we have identified the features to remove, we have a number of ways to drop the features. We can access any of the feature lists in the `removal_ops` dictionary and remove the columns manually. We also can use the `remove` method, passing in the methods that identified the features we want to remove.\n",
    "\n",
    "This method returns the resulting data which we can then use for machine learning. The original data will still be accessible in the `data` attribute of the Feature Selector.\n",
    "\n",
    "__Be careful__ of the methods used for removing features! It's a good idea to inspect the features that will be removed before using the `remove` function. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Removed 17 features.\n"
     ]
    }
   ],
   "source": [
    "train_no_missing = fs.remove(methods = ['missing'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Removed 83 features.\n"
     ]
    }
   ],
   "source": [
    "train_no_missing_zero = fs.remove(methods = ['missing', 'zero_importance'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To remove the features from all of the methods, pass in `method='all'`. Before we do this, we can check how many features will be removed using `check_removal`. This returns a list of all the features that have been idenfitied for removal. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total of 147 features identified for removal\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "['OCCUPATION_TYPE_Secretaries',\n",
       " 'FLAG_DOCUMENT_2',\n",
       " 'ORGANIZATION_TYPE_Industry: type 7',\n",
       " 'ORGANIZATION_TYPE_Trade: type 7',\n",
       " 'NAME_INCOME_TYPE_Pensioner',\n",
       " 'ORGANIZATION_TYPE_Industry: type 2',\n",
       " 'COMMONAREA_AVG',\n",
       " 'NAME_TYPE_SUITE_Children',\n",
       " 'ORGANIZATION_TYPE_Electricity',\n",
       " 'FLOORSMAX_MODE',\n",
       " 'WALLSMATERIAL_MODE_Wooden',\n",
       " 'NONLIVINGAREA_MEDI',\n",
       " 'OCCUPATION_TYPE_Low-skill Laborers',\n",
       " 'ORGANIZATION_TYPE_Industry: type 13',\n",
       " 'FLAG_DOCUMENT_10']"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_to_remove = fs.check_removal()\n",
    "all_to_remove[10:25]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can remove all of the features idenfitied."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['missing', 'single_unique', 'collinear', 'zero_importance', 'low_importance'] methods have been run\n",
      "\n",
      "Removed 147 features.\n"
     ]
    }
   ],
   "source": [
    "train_removed = fs.remove(methods = 'all')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Handling One-Hot Features\n",
    "\n",
    "If we look at the dataframe that is returned, we may notice several new columns that were not in the original data. These are created when the data is one-hot encoded for machine learning. To remove all the one-hot features, we can pass in `keep_one_hot = False` to the `remove` method. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['missing', 'single_unique', 'collinear', 'zero_importance', 'low_importance'] methods have been run\n",
      "\n",
      "Removed 190 features including one-hot features.\n"
     ]
    }
   ],
   "source": [
    "train_removed_all = fs.remove(methods = 'all', keep_one_hot=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original Number of Features 121\n",
      "Final Number of Features:  65\n"
     ]
    }
   ],
   "source": [
    "print('Original Number of Features', train.shape[1])\n",
    "print('Final Number of Features: ', train_removed_all.shape[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Alternative Option for Using all Methods\n",
    "\n",
    "If we don't want to run the identification methods one at a time, we can use `identify_all` to run all the methods in one call. For this function, we need to pass in a dictionary of parameters to use for each individual identification method. \n",
    "\n",
    "The following code accomplishes the above steps in one call."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "17 features with greater than 0.60 missing values.\n",
      "\n",
      "4 features with a single unique value.\n",
      "\n",
      "21 features with a correlation magnitude greater than 0.98.\n",
      "\n",
      "Training Gradient Boosting Model\n",
      "\n",
      "Training until validation scores don't improve for 100 rounds.\n",
      "Early stopping, best iteration is:\n",
      "[147]\tvalid_0's auc: 0.798689\n",
      "Training until validation scores don't improve for 100 rounds.\n",
      "Early stopping, best iteration is:\n",
      "[60]\tvalid_0's auc: 0.765358\n",
      "Training until validation scores don't improve for 100 rounds.\n",
      "Early stopping, best iteration is:\n",
      "[79]\tvalid_0's auc: 0.743601\n",
      "Training until validation scores don't improve for 100 rounds.\n",
      "Early stopping, best iteration is:\n",
      "[122]\tvalid_0's auc: 0.774572\n",
      "Training until validation scores don't improve for 100 rounds.\n",
      "Early stopping, best iteration is:\n",
      "[69]\tvalid_0's auc: 0.764368\n",
      "Training until validation scores don't improve for 100 rounds.\n",
      "Early stopping, best iteration is:\n",
      "[69]\tvalid_0's auc: 0.784337\n",
      "Training until validation scores don't improve for 100 rounds.\n",
      "Early stopping, best iteration is:\n",
      "[62]\tvalid_0's auc: 0.750255\n",
      "Training until validation scores don't improve for 100 rounds.\n",
      "Early stopping, best iteration is:\n",
      "[181]\tvalid_0's auc: 0.753423\n",
      "Training until validation scores don't improve for 100 rounds.\n",
      "Early stopping, best iteration is:\n",
      "[255]\tvalid_0's auc: 0.744011\n",
      "Training until validation scores don't improve for 100 rounds.\n",
      "Early stopping, best iteration is:\n",
      "[189]\tvalid_0's auc: 0.735204\n",
      "\n",
      "59 features with zero importance after one-hot encoding.\n",
      "\n",
      "130 features required for cumulative importance of 0.99 after one hot encoding.\n",
      "109 features do not contribute to cumulative importance of 0.99.\n",
      "\n",
      "136 total features out of 255 identified for removal after one-hot encoding.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "fs = FeatureSelector(data = train, labels = train_labels)\n",
    "\n",
    "fs.identify_all(selection_params = {'missing_threshold': 0.6, 'correlation_threshold': 0.98, \n",
    "                                    'task': 'classification', 'eval_metric': 'auc', \n",
    "                                     'cumulative_importance': 0.99})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['missing', 'single_unique', 'collinear', 'zero_importance', 'low_importance'] methods have been run\n",
      "\n",
      "Removed 136 features.\n"
     ]
    }
   ],
   "source": [
    "train_removed_all_once = fs.remove(methods = 'all', keep_one_hot = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>feature</th>\n",
       "      <th>importance</th>\n",
       "      <th>normalized_importance</th>\n",
       "      <th>cumulative_importance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>EXT_SOURCE_2</td>\n",
       "      <td>322.7</td>\n",
       "      <td>0.087240</td>\n",
       "      <td>0.087240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>EXT_SOURCE_3</td>\n",
       "      <td>300.2</td>\n",
       "      <td>0.081157</td>\n",
       "      <td>0.168397</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>EXT_SOURCE_1</td>\n",
       "      <td>192.2</td>\n",
       "      <td>0.051960</td>\n",
       "      <td>0.220357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>DAYS_BIRTH</td>\n",
       "      <td>170.4</td>\n",
       "      <td>0.046067</td>\n",
       "      <td>0.266423</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>DAYS_REGISTRATION</td>\n",
       "      <td>161.0</td>\n",
       "      <td>0.043525</td>\n",
       "      <td>0.309949</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             feature  importance  normalized_importance  cumulative_importance\n",
       "0       EXT_SOURCE_2       322.7               0.087240               0.087240\n",
       "1       EXT_SOURCE_3       300.2               0.081157               0.168397\n",
       "2       EXT_SOURCE_1       192.2               0.051960               0.220357\n",
       "3         DAYS_BIRTH       170.4               0.046067               0.266423\n",
       "4  DAYS_REGISTRATION       161.0               0.043525               0.309949"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fs.feature_importances.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There is a slight discrepancy between the number of features removed because the feature importances have changed. The number of features identified for removal by the `missing`, `single_unique`, and `collinear` will stay the same because they are deterministic, but the number of features from `zero_importance` and `low_importance` may vary due to training a model multiple times. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Conclusions\n",
    "\n",
    "This notebook showed how to use the `FeatureSelector` class for removing features from a dataset. There are a few important notes from this implementation:\n",
    "\n",
    "* Feature importances will change on multiple runs of the machine learning model\n",
    "* Decide whether or not to keep the extra features created from one-hot encoding\n",
    "* Try out several different values for the various parameters to decide which ones work best for a machine learning task\n",
    "* The output of missing, single unique, and collinear will stay the same for the identical parameters\n",
    "* Feature selection is a critical step of a machine learning workflow that may require several iterations to optimize\n",
    "\n",
    "I appreciate any comments, feedback, or help on this project.\n",
    "\n",
    "Will"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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